(57.024,-117.94)131T1cmrmbfLarge Language Models and Knowledge Graphs
(57.024,-133.78)131T1cmrmbf for Astronomical Entity Disambiguation
(57.024,-154.14)121T1cmrmnGolnaz Shapurian
(57.024,-254.89)121T1cmrmnThis paper presents an experiment conducted during a hackathon, focusing on using
(57.024,-270.85)121T1cmrmnlarge language models (LLMs) and knowledge graph clustering to extract entities and
(57.024,-286.69)121T1cmrmnrelationships from astronomical text. The study demonstrates an approach to
(57.024,-302.53)121T1cmrmndisambiguate entities that can appear in various contexts within the astronomical
(57.024,-318.37)121T1cmrmndomain. By collecting excerpts around specific entities and leveraging the GPT-4
(57.024,-334.33)121T1cmrmnlanguage model, relevant entities and relationships are extracted. The extracted
(57.024,-350.17)121T1cmrmninformation is then used to construct a knowledge graph, which is clustered using the
(57.024,-366.01)121T1cmrmnLeiden algorithm. The resulting Leiden communities are utilized to identify the
(57.024,-381.85)121T1cmrmnpercentage of association of unknown excerpts to each community, thereby enabling
(57.024,-397.81)121T1cmrmndisambiguation. The experiment showcases the potential of combining LLMs and
(57.024,-413.67)121T1cmrmnknowledge graph clustering techniques for information extraction in astronomical
(57.024,-429.51)121T1cmrmnresearch. The results highlight the effectiveness of the approach in identifying and
(57.024,-445.35)121T1cmrmndisambiguating entities, as well as grouping them into meaningful clusters based on
(57.024,-461.31)121T1cmrmntheir relationships.
(57.024,-507.15)121T1cmrmn1. Introduction
(57.024,-537.39)121T1cmrmnEntity disambiguation is a critical challenge in processing astronomical text data, such
(57.024,-553.23)121T1cmrmnas scientific publications, technical reports, and observational logs. For instance, the
(57.024,-569.07)121T1cmrmnentity "Apollo" can refer to missions, craters, or even an asteroid. Accurate
(57.024,-585.06)121T1cmrmndisambiguation of such entities is essential for efficient information retrieval, knowledge
(57.024,-600.9)121T1cmrmndiscovery, and text mining within the astronomical domain. Failure to do so can lead to
(57.024,-616.74)121T1cmrmnconfusion, misinterpretation of information, and hinder research progress (Brescia and
(57.024,-632.58)121T1cmrmnLongo, 2012).
(57.024,-662.94)121T1cmrmnKnowledge graphs (KGs) have emerged as a powerful tool for addressing this challenge
(57.024,-678.78)121T1cmrmnby representing and organizing complex information in a structured and machine-
(57.024,-694.616)121T1cmrmnreadable format. In astronomical research, KGs can integrate data from diverse

(57.024,-73.29999)121T1cmrmnsources, uncover hidden patterns and relationships, and support decision-making (Khoo
(57.024,-89.14001)121T1cmrmnet al., 2023). By representing entities and their relationships in a graph structure,
(57.024,-104.98)121T1cmrmnresearchers can gain new insights, formulate hypotheses, and promote knowledge
(57.024,-120.94)121T1cmrmnsharing across different subdomains of astronomy.
(57.024,-151.18)121T1cmrmnLarge language models (LLMs), such as GPT-4, trained on extensive textual datasets,
(57.024,-167.02)121T1cmrmnexhibit exceptional proficiency in comprehending and producing human-like language.
(57.024,-182.86)121T1cmrmnBy harnessing the knowledge captured in LLMs, it is possible to extract entities and
(57.024,-198.82)121T1cmrmnrelationships from text with high accuracy and efficiency, reducing the reliance on
(57.024,-214.66)121T1cmrmnmanual annotation and rule-based approaches.
(57.024,-244.93)121T1cmrmnThis experiment investigates the use of the GPT-4 language model to extract entities
(57.024,-260.77)121T1cmrmnand relationships from astronomical text, focusing on entity disambiguation. A
(57.024,-276.73)121T1cmrmnknowledge graph is constructed from the extracted information to capture the
(57.024,-292.57)121T1cmrmnconnections between various astronomical concepts. The Leiden algorithm is then
(57.024,-308.41)121T1cmrmnapplied to cluster the knowledge graph, identifying communities of related entities and
(57.024,-324.25)121T1cmrmnrelationships (Traag et al., 2019; Edge et al., 2024). These communities are used to
(57.024,-340.21)121T1cmrmndisambiguate unknown excerpts by calculating their association percentages.
(57.024,-370.45)121T1cmrmnBy evaluating the effectiveness of this approach, this study demonstrates the potential
(57.024,-386.29)121T1cmrmnof combining LLMs and knowledge graph clustering techniques for entity
(57.024,-402.13)121T1cmrmndisambiguation and information extraction in astronomical text. The findings can
(57.024,-418.11)121T1cmrmncontribute to developing more efficient and accurate tools for knowledge discovery and
(57.024,-433.95)121T1cmrmnexploration in astronomy, leading to better information retrieval, more precise data
(57.024,-449.79)121T1cmrmnintegration, and a deeper understanding of astronomical phenomena. This approach
(57.024,-465.63)121T1cmrmnstreamlines the process of extracting insights from astronomical literature, ultimately
(57.024,-481.47)121T1cmrmnaccelerating scientific progress.
(57.024,-527.43)121T1cmrmn2. Related Work
(57.024,-557.67)121T1cmrmnThe integration of LLMs and KGs has gained significant attention as researchers
(57.024,-573.54)121T1cmrmnexplore combining these two powerful technologies. LLMs, such as GPT-4, have
(57.024,-589.38)121T1cmrmndemonstrated remarkable abilities in natural language processing and generation, while
(57.024,-605.34)121T1cmrmnKGs provide structured representations of knowledge that can enhance reasoning and
(57.024,-621.18)121T1cmrmninference capabilities.
(57.024,-651.42)121T1cmrmnPan et al. (2023) present a roadmap for the unification of LLMs and KGs, outlining three
(57.024,-667.26)121T1cmrmngeneral frameworks: KG-enhanced LLMs, LLM-augmented KGs, and Synergized LLMs
(57.024,-683.216)121T1cmrmn+ KGs. They review existing efforts within these frameworks and highlight future
(57.024,-699.056)121T1cmrmnresearch directions, emphasizing the complementary nature of LLMs and KGs. Shu et

(57.024,-73.29999)121T1cmrmnal. (2024) introduce the Knowledge Graph Large Language Model Framework,
(57.024,-89.14001)121T1cmrmnleveraging chain-of-thought prompting and in-context learning to enhance multi-hop link
(57.024,-104.98)121T1cmrmnprediction in KGs.
(57.024,-135.34)121T1cmrmnSeveral studies have explored LLMs in supporting Knowledge Graph Engineering
(57.024,-151.18)121T1cmrmn(KGE). Meyer et al. (2023) demonstrate how ChatGPT can assist in the development
(57.024,-167.02)121T1cmrmnand management of KGs. Trajanoska et al. (2023) analyze how advanced LLMs, like
(57.024,-182.86)121T1cmrmnChatGPT, compare with specialized pretrained models for joint entity and relation
(57.024,-198.82)121T1cmrmnextraction. They find that using advanced LLM models can improve the accuracy of the
(57.024,-214.66)121T1cmrmnautomatic creation of KGs from raw texts.
(57.024,-244.93)121T1cmrmnJia et al. (2024) propose a framework integrating the Deep Document Model and KG-
(57.024,-260.77)121T1cmrmnenhanced Query Processing for optimized complex query handling in scholarly
(57.024,-276.73)121T1cmrmnknowledge graphs. Their approach, combining the ANU Scholarly Knowledge Graph
(57.024,-292.57)121T1cmrmnwith LLMs, enhances knowledge utilization and natural language understanding.
(57.024,-322.81)121T1cmrmnThe use of KGs as factual context for LLM prompts has also been investigated. Abu-
(57.024,-338.65)121T1cmrmnRasheed et al. (2024) explore this concept in personalized education, utilizing semantic
(57.024,-354.61)121T1cmrmnrelations in the KG to offer curated knowledge about learning recommendations. Their
(57.024,-370.45)121T1cmrmnresults show enhanced recall and precision of generated explanations compared to
(57.024,-386.29)121T1cmrmnthose generated solely by GPT.
(57.024,-416.55)121T1cmrmnYang et al. (2023) review studies on integrating pre-trained language models with KGs,
(57.024,-432.51)121T1cmrmndetailing existing KG-enhanced models and applications. They propose developing KG-
(57.024,-448.35)121T1cmrmninfused LLMs to further improve their factual reasoning ability.
(57.024,-478.59)121T1cmrmnIn astronomical research, Sun et al. (2024) demonstrate using LLMs to extract concepts
(57.024,-494.43)121T1cmrmnfrom publications and form a KG. Their knowledge graph reveals two phases of
(57.024,-510.27)121T1cmrmndevelopment: technology integration and exploration in scientific discovery.
(57.024,-540.63)121T1cmrmnBuilding upon Trajanoska et al. (2023), this experiment extends their approach by
(57.024,-556.47)121T1cmrmnincorporating entity disambiguation and contextual understanding within the
(57.024,-572.34)121T1cmrmnastronomical domain. While their study focused on KG construction, this work leverages
(57.024,-588.18)121T1cmrmnLLMs' natural language processing strengths and KGs' structured knowledge
(57.024,-604.14)121T1cmrmnrepresentation to enhance the accuracy and relevance of knowledge extraction from
(57.024,-619.98)121T1cmrmnastronomical text. The key contribution lies in applying the Leiden algorithm for
(57.024,-635.82)121T1cmrmncommunity detection within the KG, enabling effective disambiguation of entities in
(57.024,-651.66)121T1cmrmndifferent astronomical contexts. By integrating advanced clustering techniques with KG
(57.024,-667.62)121T1cmrmnconstruction, this study aims to improve the precision and contextual understanding of
(57.024,-683.456)121T1cmrmnextracted entities, ultimately facilitating more accurate and efficient knowledge discovery
(57.024,-699.296)121T1cmrmnin astronomy.

(57.024,-73.29999)121T1cmrmn3. Data Collection
(57.024,-103.54)121T1cmrmnThe data collection process involved querying the Astrophysics Data System (ADS)
(57.024,-119.38)121T1cmrmnusing its application programming interface (Kurtz et al., 2000) to gather textual data
(57.024,-135.34)121T1cmrmnrelated to the astronomical entity "Apollo." Two separate queries were performed to
(57.024,-151.18)121T1cmrmncapture different contexts in which the term appears.
(57.024,-181.42)121T1cmrmnThe first query focused on retrieving documents related to Apollo craters on the Moon
(57.024,-197.26)121T1cmrmnby searching for the full text of articles containing the words "Apollo," "Crater," and
(57.024,-213.22)121T1cmrmn"Moon," limited to planetary science journals.
(57.024,-243.49)121T1cmrmnThe second query aimed to gather documents related to Apollo missions, searching for
(57.024,-259.33)121T1cmrmnthe full text of articles containing "Apollo," "mission," and any Apollo mission name from
(57.024,-275.17)121T1cmrmna Wikipedia-sourced list.
(57.024,-305.53)121T1cmrmnThe records from both queries were combined, and a regular expression-based
(57.024,-321.37)121T1cmrmnapproach extracted excerpts of at most 256 words, consisting of complete sentences,
(57.024,-337.21)121T1cmrmnsurrounding each occurrence of "Apollo." Quality control measures, including manual
(57.024,-353.05)121T1cmrmnreview to filter out irrelevant or low-quality documents and maintain balance between
(57.024,-369.01)121T1cmrmnApollo crater and Apollo mission contexts, resulted in approximately 800 excerpts.
(57.024,-399.25)121T1cmrmnNo extensive data cleaning or preprocessing was performed on the extracted excerpts
(57.024,-415.11)121T1cmrmnto preserve the authenticity and contextual information associated with each occurrence
(57.024,-430.95)121T1cmrmnof the term "Apollo."
(57.024,-461.31)121T1cmrmnThis curated collection of excerpts, each containing the term "Apollo" and its
(57.024,-477.15)121T1cmrmnsurrounding context from both Apollo crater and Apollo mission-related documents,
(57.024,-492.99)121T1cmrmnserved as the foundation for subsequent relationship extraction and entity
(57.024,-508.83)121T1cmrmndisambiguation using large language models and knowledge graph clustering
(57.024,-570.63)121T1cmrmn4. Methodology
(57.024,-600.9)121T1cmrmnThe experiment methodology involved using the GPT-4 language model for entity and
(57.024,-616.74)121T1cmrmnrelationship extraction, constructing a knowledge graph, and applying the Leiden
(57.024,-632.58)121T1cmrmnalgorithm for community detection and entity disambiguation.
(57.024,-662.94)121T1cmrmnThe GPT-4 language model was accessed through the OpenAI API, using a specialized
(57.024,-678.78)121T1cmrmnprompt containing instructions and two examples relevant to astronomy (see Appendix).
(57.024,-694.616)121T1cmrmnThe prompt, adapted from Trajanoska et al. (2023), included representative examples

(57.024,-73.29999)121T1cmrmnshowcasing the desired input-output format and types of entities and relationships to be
(57.024,-89.14001)121T1cmrmnextracted. One example focused on Apollo missions, while the other revolved around
(57.024,-104.98)121T1cmrmnApollo craters. Each example consisted of a short text passage, followed by a
(57.024,-120.94)121T1cmrmnstructured representation of the extracted entities and their relationships, labeled with
(57.024,-136.78)121T1cmrmnpredefined categories (e.g., celestial objects, missions, instruments, spacecraft) and
(57.024,-152.62)121T1cmrmntheir semantic connections.
(57.024,-182.86)121T1cmrmnTo ensure focused and deterministic responses, the GPT-4 model's temperature
(57.024,-198.82)121T1cmrmnparameter was set to 0. The 256-word excerpts containing the term "Apollo" were then
(57.024,-214.66)121T1cmrmnprocessed by the model, extracting relevant entities and relationships based on the
(57.024,-230.5)121T1cmrmnastronomical context guided by the prompt.
(57.024,-260.77)121T1cmrmnA knowledge graph was constructed to organize the extracted entities and relationships,
(57.024,-276.73)121T1cmrmnwith nodes representing entities and edges representing relationships. This facilitated
(57.024,-292.57)121T1cmrmnvisualization and exploration of the interconnections between different astronomical
(57.024,-308.41)121T1cmrmnconcepts related to Apollo craters and Apollo missions.

[color_283006][even odd rule]
(57pt, -674.41pt) – (552.75pt, -674.41pt)
– (552.75pt, -674.41pt)
– (552.75pt, -327.36pt)
– (552.75pt, -327.36pt)
– (57pt, -327.36pt) – cycle
[color_283006][even odd rule]
(73.789pt, -671.28pt) – (542.239pt, -671.28pt)
– (542.239pt, -671.28pt)
– (542.239pt, -624.335pt)
– (542.239pt, -624.335pt)
– (73.789pt, -624.335pt) – cycle

(81.264,-637.62)9.961T1cmrmnFigure 1: Knowledge graph constructed from the Apollo dataset, showing relationships between
(81.264,-649.98)9.961T1cmrmnentities such as celestial objects, missions, and instruments, with nodes representing entities and
(81.264,-662.46)9.961T1cmrmnedges representing their relationships.

(57.024,-73.29999)121T1cmrmnThe Leiden algorithm was applied for community detection within the knowledge graph.
(57.024,-89.14001)121T1cmrmnThis clustering technique identified groups of densely connected nodes, uncovering
(57.024,-104.98)121T1cmrmnclusters of semantically related entities and relationships likely belonging to the same
(57.024,-120.94)121T1cmrmncontext (e.g., Apollo craters or Apollo missions).
(57.024,-428.67)121T1cmrmnEntity disambiguation was performed using the detected communities. When a new
(57.024,-444.51)121T1cmrmnexcerpt containing the term "Apollo" was encountered, its extracted entities and
(57.024,-460.47)121T1cmrmnrelationships were compared against existing communities. By calculating the
(57.024,-476.31)121T1cmrmnpercentage of association between the new excerpt and each community, the most
(57.024,-492.15)121T1cmrmnlikely context for the term "Apollo" was determined, effectively disambiguating whether it
(57.024,-507.99)121T1cmrmnreferred to a crater or a mission.
(57.024,-553.95)121T1cmrmn5. Results and Discussion
(57.024,-584.22)121T1cmrmnThe experiment results demonstrate the effectiveness of combining large language
(57.024,-600.06)121T1cmrmnmodels and knowledge graph clustering for entity disambiguation and contextual
(57.024,-615.9)121T1cmrmnunderstanding in astronomy. The clustering algorithm successfully identified three
(57.024,-631.86)121T1cmrmndistinct clusters based on entity associations with Apollo craters and missions.
(57.024,-662.1)121T1cmrmnTable 1 presents the distribution of crater and mission entities within each cluster.
(57.024,-677.94)121T1cmrmnCluster 0 was predominantly associated with mission entities (73.63%), while Clusters 1
(57.024,-693.776)121T1cmrmnand 2 were more strongly associated with crater entities (92.46% and 81.33%,

[color_283006][even odd rule]
(57pt, -400.27pt) – (530.45pt, -400.27pt)
– (530.45pt, -400.27pt)
– (530.45pt, -139.87pt)
– (530.45pt, -139.87pt)
– (57pt, -139.87pt) – cycle

(172.35pt, -356.05pt) – (388.22pt, -356.05pt)
– (388.22pt, -356.05pt)
– (388.22pt, -140.73pt)
– (388.22pt, -140.73pt)
– (172.35pt, -140.73pt) – cycle

[color_283006][even odd rule]
(76.365pt, -398.3pt) – (513.005pt, -398.3pt)
– (513.005pt, -398.3pt)
– (513.005pt, -361.276pt)
– (513.005pt, -361.276pt)
– (76.365pt, -361.276pt) – cycle

(83.784,-375.61)11.041T1cmrmnFigure 2:  Clusters identified by the Leiden algorithm within the knowledge graph, highlighting
(83.784,-390.13)11.041T1cmrmnthree distinct groups of densely connected entities related to Apollo missions, and craters.

(57.024,-73.29999)121T1cmrmnrespectively). The high percentages of crater-related entities in Clusters 1 and 2 indicate
(57.024,-89.14001)121T1cmrmnthe approach's success in disambiguating and grouping entities from crater excerpts.
(208.26,-119.98)9.961T1cmrmnCrater Entities (%)
(338.26,-119.98)9.961T1cmrmnMission Entities (%)

[color_269680][even odd rule]
(119.78pt, -110.5pt) – (120.26pt, -110.5pt)
– (120.26pt, -110.5pt)
– (120.26pt, -108.1pt)
– (120.26pt, -108.1pt)
– (119.78pt, -108.1pt) – cycle
[color_269680][even odd rule]
(119.78pt, -108.58pt) – (120.26pt, -108.58pt)
– (120.26pt, -108.58pt)
– (120.26pt, -108.1pt)
– (120.26pt, -108.1pt)
– (119.78pt, -108.1pt) – cycle
[color_269680][even odd rule]
(120.26pt, -108.58pt) – (178.844pt, -108.58pt)
– (178.844pt, -108.58pt)
– (178.844pt, -108.1pt)
– (178.844pt, -108.1pt)
– (120.26pt, -108.1pt) – cycle
[color_269680][even odd rule]
(178.85pt, -108.58pt) – (179.33pt, -108.58pt)
– (179.33pt, -108.58pt)
– (179.33pt, -108.1pt)
– (179.33pt, -108.1pt)
– (178.85pt, -108.1pt) – cycle
[color_269680][even odd rule]
(179.33pt, -108.58pt) – (317.59pt, -108.58pt)
– (317.59pt, -108.58pt)
– (317.59pt, -108.1pt)
– (317.59pt, -108.1pt)
– (179.33pt, -108.1pt) – cycle
[color_269680][even odd rule]
(317.59pt, -108.58pt) – (318.07pt, -108.58pt)
– (318.07pt, -108.58pt)
– (318.07pt, -108.1pt)
– (318.07pt, -108.1pt)
– (317.59pt, -108.1pt) – cycle
[color_269680][even odd rule]
(318.07pt, -108.58pt) – (451.17pt, -108.58pt)
– (451.17pt, -108.58pt)
– (451.17pt, -108.1pt)
– (451.17pt, -108.1pt)
– (318.07pt, -108.1pt) – cycle
[color_269680][even odd rule]
(451.18pt, -110.5pt) – (451.66pt, -110.5pt)
– (451.66pt, -110.5pt)
– (451.66pt, -108.1pt)
– (451.66pt, -108.1pt)
– (451.18pt, -108.1pt) – cycle
[color_269680][even odd rule]
(451.18pt, -108.58pt) – (451.66pt, -108.58pt)
– (451.66pt, -108.58pt)
– (451.66pt, -108.1pt)
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– (451.18pt, -108.1pt) – cycle
[color_269680][even odd rule]
(120.74pt, -109.78pt) – (121.22pt, -109.78pt)
– (121.22pt, -109.78pt)
– (121.22pt, -109.3pt)
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– (120.74pt, -109.3pt) – cycle
[color_269680][even odd rule]
(120.74pt, -109.78pt) – (121.22pt, -109.78pt)
– (121.22pt, -109.78pt)
– (121.22pt, -109.3pt)
– (121.22pt, -109.3pt)
– (120.74pt, -109.3pt) – cycle
[color_269680][even odd rule]
(121.22pt, -109.78pt) – (178.004pt, -109.78pt)
– (178.004pt, -109.78pt)
– (178.004pt, -109.3pt)
– (178.004pt, -109.3pt)
– (121.22pt, -109.3pt) – cycle
[color_269680][even odd rule]
(178.01pt, -109.78pt) – (178.49pt, -109.78pt)
– (178.49pt, -109.78pt)
– (178.49pt, -109.3pt)
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[color_269680][even odd rule]
(178.01pt, -109.78pt) – (178.49pt, -109.78pt)
– (178.49pt, -109.78pt)
– (178.49pt, -109.3pt)
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[color_269680][even odd rule]
(120.74pt, -123.34pt) – (121.22pt, -123.34pt)
– (121.22pt, -123.34pt)
– (121.22pt, -122.86pt)
– (121.22pt, -122.86pt)
– (120.74pt, -122.86pt) – cycle
[color_269680][even odd rule]
(120.74pt, -123.34pt) – (121.22pt, -123.34pt)
– (121.22pt, -123.34pt)
– (121.22pt, -122.86pt)
– (121.22pt, -122.86pt)
– (120.74pt, -122.86pt) – cycle
[color_269680][even odd rule]
(121.22pt, -123.34pt) – (178.004pt, -123.34pt)
– (178.004pt, -123.34pt)
– (178.004pt, -122.86pt)
– (178.004pt, -122.86pt)
– (121.22pt, -122.86pt) – cycle
[color_269680][even odd rule]
(178.01pt, -123.34pt) – (178.49pt, -123.34pt)
– (178.49pt, -123.34pt)
– (178.49pt, -122.86pt)
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[color_269680][even odd rule]
(178.01pt, -123.34pt) – (178.49pt, -123.34pt)
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– (178.49pt, -122.86pt)
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[color_269680][even odd rule]
(120.74pt, -122.86pt) – (121.22pt, -122.86pt)
– (121.22pt, -122.86pt)
– (121.22pt, -109.78pt)
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– (120.74pt, -109.78pt) – cycle
[color_269680][even odd rule]
(178.01pt, -122.86pt) – (178.49pt, -122.86pt)
– (178.49pt, -122.86pt)
– (178.49pt, -109.78pt)
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– (178.01pt, -109.78pt) – cycle
[color_269680][even odd rule]
(119.78pt, -123.7pt) – (120.26pt, -123.7pt)
– (120.26pt, -123.7pt)
– (120.26pt, -110.5pt)
– (120.26pt, -110.5pt)
– (119.78pt, -110.5pt) – cycle
[color_269680][even odd rule]
(179.21pt, -109.78pt) – (179.69pt, -109.78pt)
– (179.69pt, -109.78pt)
– (179.69pt, -109.3pt)
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– (179.21pt, -109.3pt) – cycle
[color_269680][even odd rule]
(179.21pt, -109.78pt) – (179.69pt, -109.78pt)
– (179.69pt, -109.78pt)
– (179.69pt, -109.3pt)
– (179.69pt, -109.3pt)
– (179.21pt, -109.3pt) – cycle
[color_269680][even odd rule]
(179.69pt, -109.78pt) – (316.75pt, -109.78pt)
– (316.75pt, -109.78pt)
– (316.75pt, -109.3pt)
– (316.75pt, -109.3pt)
– (179.69pt, -109.3pt) – cycle
[color_269680][even odd rule]
(316.75pt, -109.78pt) – (317.23pt, -109.78pt)
– (317.23pt, -109.78pt)
– (317.23pt, -109.3pt)
– (317.23pt, -109.3pt)
– (316.75pt, -109.3pt) – cycle
[color_269680][even odd rule]
(316.75pt, -109.78pt) – (317.23pt, -109.78pt)
– (317.23pt, -109.78pt)
– (317.23pt, -109.3pt)
– (317.23pt, -109.3pt)
– (316.75pt, -109.3pt) – cycle
[color_269680][even odd rule]
(179.21pt, -123.34pt) – (179.69pt, -123.34pt)
– (179.69pt, -123.34pt)
– (179.69pt, -122.86pt)
– (179.69pt, -122.86pt)
– (179.21pt, -122.86pt) – cycle
[color_269680][even odd rule]
(179.21pt, -123.34pt) – (179.69pt, -123.34pt)
– (179.69pt, -123.34pt)
– (179.69pt, -122.86pt)
– (179.69pt, -122.86pt)
– (179.21pt, -122.86pt) – cycle
[color_269680][even odd rule]
(179.69pt, -123.34pt) – (316.75pt, -123.34pt)
– (316.75pt, -123.34pt)
– (316.75pt, -122.86pt)
– (316.75pt, -122.86pt)
– (179.69pt, -122.86pt) – cycle
[color_269680][even odd rule]
(316.75pt, -123.34pt) – (317.23pt, -123.34pt)
– (317.23pt, -123.34pt)
– (317.23pt, -122.86pt)
– (317.23pt, -122.86pt)
– (316.75pt, -122.86pt) – cycle
[color_269680][even odd rule]
(316.75pt, -123.34pt) – (317.23pt, -123.34pt)
– (317.23pt, -123.34pt)
– (317.23pt, -122.86pt)
– (317.23pt, -122.86pt)
– (316.75pt, -122.86pt) – cycle
[color_269680][even odd rule]
(179.21pt, -122.86pt) – (179.69pt, -122.86pt)
– (179.69pt, -122.86pt)
– (179.69pt, -109.78pt)
– (179.69pt, -109.78pt)
– (179.21pt, -109.78pt) – cycle
[color_269680][even odd rule]
(316.75pt, -122.86pt) – (317.23pt, -122.86pt)
– (317.23pt, -122.86pt)
– (317.23pt, -109.78pt)
– (317.23pt, -109.78pt)
– (316.75pt, -109.78pt) – cycle
[color_269680][even odd rule]
(317.95pt, -109.78pt) – (318.43pt, -109.78pt)
– (318.43pt, -109.78pt)
– (318.43pt, -109.3pt)
– (318.43pt, -109.3pt)
– (317.95pt, -109.3pt) – cycle
[color_269680][even odd rule]
(317.95pt, -109.78pt) – (318.43pt, -109.78pt)
– (318.43pt, -109.78pt)
– (318.43pt, -109.3pt)
– (318.43pt, -109.3pt)
– (317.95pt, -109.3pt) – cycle
[color_269680][even odd rule]
(318.43pt, -109.78pt) – (450.21pt, -109.78pt)
– (450.21pt, -109.78pt)
– (450.21pt, -109.3pt)
– (450.21pt, -109.3pt)
– (318.43pt, -109.3pt) – cycle
[color_269680][even odd rule]
(450.22pt, -109.78pt) – (450.7pt, -109.78pt)
– (450.7pt, -109.78pt)
– (450.7pt, -109.3pt)
– (450.7pt, -109.3pt)
– (450.22pt, -109.3pt) – cycle
[color_269680][even odd rule]
(450.22pt, -109.78pt) – (450.7pt, -109.78pt)
– (450.7pt, -109.78pt)
– (450.7pt, -109.3pt)
– (450.7pt, -109.3pt)
– (450.22pt, -109.3pt) – cycle
[color_269680][even odd rule]
(317.95pt, -123.34pt) – (318.43pt, -123.34pt)
– (318.43pt, -123.34pt)
– (318.43pt, -122.86pt)
– (318.43pt, -122.86pt)
– (317.95pt, -122.86pt) – cycle
[color_269680][even odd rule]
(317.95pt, -123.34pt) – (318.43pt, -123.34pt)
– (318.43pt, -123.34pt)
– (318.43pt, -122.86pt)
– (318.43pt, -122.86pt)
– (317.95pt, -122.86pt) – cycle
[color_269680][even odd rule]
(318.43pt, -123.34pt) – (450.21pt, -123.34pt)
– (450.21pt, -123.34pt)
– (450.21pt, -122.86pt)
– (450.21pt, -122.86pt)
– (318.43pt, -122.86pt) – cycle
[color_269680][even odd rule]
(450.22pt, -123.34pt) – (450.7pt, -123.34pt)
– (450.7pt, -123.34pt)
– (450.7pt, -122.86pt)
– (450.7pt, -122.86pt)
– (450.22pt, -122.86pt) – cycle
[color_269680][even odd rule]
(450.22pt, -123.34pt) – (450.7pt, -123.34pt)
– (450.7pt, -123.34pt)
– (450.7pt, -122.86pt)
– (450.7pt, -122.86pt)
– (450.22pt, -122.86pt) – cycle
[color_269680][even odd rule]
(317.95pt, -122.86pt) – (318.43pt, -122.86pt)
– (318.43pt, -122.86pt)
– (318.43pt, -109.78pt)
– (318.43pt, -109.78pt)
– (317.95pt, -109.78pt) – cycle
[color_269680][even odd rule]
(450.22pt, -122.86pt) – (450.7pt, -122.86pt)
– (450.7pt, -122.86pt)
– (450.7pt, -109.78pt)
– (450.7pt, -109.78pt)
– (450.22pt, -109.78pt) – cycle
[color_269680][even odd rule]
(451.18pt, -123.7pt) – (451.66pt, -123.7pt)
– (451.66pt, -123.7pt)
– (451.66pt, -110.5pt)
– (451.66pt, -110.5pt)
– (451.18pt, -110.5pt) – cycle

(121.22pt, -136.78pt) – (178.004pt, -136.78pt)
– (178.004pt, -136.78pt)
– (178.004pt, -125.26pt)
– (178.004pt, -125.26pt)
– (121.22pt, -125.26pt) – cycle

(121.22pt, -136.78pt) – (178.004pt, -136.78pt)
– (178.004pt, -136.78pt)
– (178.004pt, -125.26pt)
– (178.004pt, -125.26pt)
– (121.22pt, -125.26pt) – cycle

[color_269680][even odd rule]
(119.78pt, -125.26pt) – (120.26pt, -125.26pt)
– (120.26pt, -125.26pt)
– (120.26pt, -123.7pt)
– (120.26pt, -123.7pt)
– (119.78pt, -123.7pt) – cycle
[color_269680][even odd rule]
(451.18pt, -125.26pt) – (451.66pt, -125.26pt)
– (451.66pt, -125.26pt)
– (451.66pt, -123.7pt)
– (451.66pt, -123.7pt)
– (451.18pt, -123.7pt) – cycle
[color_269680][even odd rule]
(120.74pt, -124.54pt) – (121.22pt, -124.54pt)
– (121.22pt, -124.54pt)
– (121.22pt, -124.06pt)
– (121.22pt, -124.06pt)
– (120.74pt, -124.06pt) – cycle
[color_269680][even odd rule]
(120.74pt, -124.54pt) – (121.22pt, -124.54pt)
– (121.22pt, -124.54pt)
– (121.22pt, -124.06pt)
– (121.22pt, -124.06pt)
– (120.74pt, -124.06pt) – cycle
[color_269680][even odd rule]
(121.22pt, -124.54pt) – (178.004pt, -124.54pt)
– (178.004pt, -124.54pt)
– (178.004pt, -124.06pt)
– (178.004pt, -124.06pt)
– (121.22pt, -124.06pt) – cycle
[color_269680][even odd rule]
(178.01pt, -124.54pt) – (178.49pt, -124.54pt)
– (178.49pt, -124.54pt)
– (178.49pt, -124.06pt)
– (178.49pt, -124.06pt)
– (178.01pt, -124.06pt) – cycle
[color_269680][even odd rule]
(178.01pt, -124.54pt) – (178.49pt, -124.54pt)
– (178.49pt, -124.54pt)
– (178.49pt, -124.06pt)
– (178.49pt, -124.06pt)
– (178.01pt, -124.06pt) – cycle
[color_269680][even odd rule]
(120.74pt, -138.1pt) – (121.22pt, -138.1pt)
– (121.22pt, -138.1pt)
– (121.22pt, -137.62pt)
– (121.22pt, -137.62pt)
– (120.74pt, -137.62pt) – cycle
[color_269680][even odd rule]
(120.74pt, -138.1pt) – (121.22pt, -138.1pt)
– (121.22pt, -138.1pt)
– (121.22pt, -137.62pt)
– (121.22pt, -137.62pt)
– (120.74pt, -137.62pt) – cycle
[color_269680][even odd rule]
(121.22pt, -138.1pt) – (178.004pt, -138.1pt)
– (178.004pt, -138.1pt)
– (178.004pt, -137.62pt)
– (178.004pt, -137.62pt)
– (121.22pt, -137.62pt) – cycle
[color_269680][even odd rule]
(178.01pt, -138.1pt) – (178.49pt, -138.1pt)
– (178.49pt, -138.1pt)
– (178.49pt, -137.62pt)
– (178.49pt, -137.62pt)
– (178.01pt, -137.62pt) – cycle
[color_269680][even odd rule]
(178.01pt, -138.1pt) – (178.49pt, -138.1pt)
– (178.49pt, -138.1pt)
– (178.49pt, -137.62pt)
– (178.49pt, -137.62pt)
– (178.01pt, -137.62pt) – cycle
[color_269680][even odd rule]
(120.74pt, -137.62pt) – (121.22pt, -137.62pt)
– (121.22pt, -137.62pt)
– (121.22pt, -124.54pt)
– (121.22pt, -124.54pt)
– (120.74pt, -124.54pt) – cycle
[color_269680][even odd rule]
(178.01pt, -137.62pt) – (178.49pt, -137.62pt)
– (178.49pt, -137.62pt)
– (178.49pt, -124.54pt)
– (178.49pt, -124.54pt)
– (178.01pt, -124.54pt) – cycle
[color_269680][even odd rule]
(119.78pt, -138.46pt) – (120.26pt, -138.46pt)
– (120.26pt, -138.46pt)
– (120.26pt, -125.26pt)
– (120.26pt, -125.26pt)
– (119.78pt, -125.26pt) – cycle
[color_269680][even odd rule]
(179.21pt, -124.54pt) – (179.69pt, -124.54pt)
– (179.69pt, -124.54pt)
– (179.69pt, -124.06pt)
– (179.69pt, -124.06pt)
– (179.21pt, -124.06pt) – cycle
[color_269680][even odd rule]
(179.21pt, -124.54pt) – (179.69pt, -124.54pt)
– (179.69pt, -124.54pt)
– (179.69pt, -124.06pt)
– (179.69pt, -124.06pt)
– (179.21pt, -124.06pt) – cycle
[color_269680][even odd rule]
(179.69pt, -124.54pt) – (316.75pt, -124.54pt)
– (316.75pt, -124.54pt)
– (316.75pt, -124.06pt)
– (316.75pt, -124.06pt)
– (179.69pt, -124.06pt) – cycle
[color_269680][even odd rule]
(316.75pt, -124.54pt) – (317.23pt, -124.54pt)
– (317.23pt, -124.54pt)
– (317.23pt, -124.06pt)
– (317.23pt, -124.06pt)
– (316.75pt, -124.06pt) – cycle
[color_269680][even odd rule]
(316.75pt, -124.54pt) – (317.23pt, -124.54pt)
– (317.23pt, -124.54pt)
– (317.23pt, -124.06pt)
– (317.23pt, -124.06pt)
– (316.75pt, -124.06pt) – cycle
[color_269680][even odd rule]
(179.21pt, -138.1pt) – (179.69pt, -138.1pt)
– (179.69pt, -138.1pt)
– (179.69pt, -137.62pt)
– (179.69pt, -137.62pt)
– (179.21pt, -137.62pt) – cycle
[color_269680][even odd rule]
(179.21pt, -138.1pt) – (179.69pt, -138.1pt)
– (179.69pt, -138.1pt)
– (179.69pt, -137.62pt)
– (179.69pt, -137.62pt)
– (179.21pt, -137.62pt) – cycle
[color_269680][even odd rule]
(179.69pt, -138.1pt) – (316.75pt, -138.1pt)
– (316.75pt, -138.1pt)
– (316.75pt, -137.62pt)
– (316.75pt, -137.62pt)
– (179.69pt, -137.62pt) – cycle
[color_269680][even odd rule]
(316.75pt, -138.1pt) – (317.23pt, -138.1pt)
– (317.23pt, -138.1pt)
– (317.23pt, -137.62pt)
– (317.23pt, -137.62pt)
– (316.75pt, -137.62pt) – cycle
[color_269680][even odd rule]
(316.75pt, -138.1pt) – (317.23pt, -138.1pt)
– (317.23pt, -138.1pt)
– (317.23pt, -137.62pt)
– (317.23pt, -137.62pt)
– (316.75pt, -137.62pt) – cycle
[color_269680][even odd rule]
(179.21pt, -137.62pt) – (179.69pt, -137.62pt)
– (179.69pt, -137.62pt)
– (179.69pt, -124.54pt)
– (179.69pt, -124.54pt)
– (179.21pt, -124.54pt) – cycle
[color_269680][even odd rule]
(316.75pt, -137.62pt) – (317.23pt, -137.62pt)
– (317.23pt, -137.62pt)
– (317.23pt, -124.54pt)
– (317.23pt, -124.54pt)
– (316.75pt, -124.54pt) – cycle
[color_269680][even odd rule]
(317.95pt, -124.54pt) – (318.43pt, -124.54pt)
– (318.43pt, -124.54pt)
– (318.43pt, -124.06pt)
– (318.43pt, -124.06pt)
– (317.95pt, -124.06pt) – cycle
[color_269680][even odd rule]
(317.95pt, -124.54pt) – (318.43pt, -124.54pt)
– (318.43pt, -124.54pt)
– (318.43pt, -124.06pt)
– (318.43pt, -124.06pt)
– (317.95pt, -124.06pt) – cycle
[color_269680][even odd rule]
(318.43pt, -124.54pt) – (450.21pt, -124.54pt)
– (450.21pt, -124.54pt)
– (450.21pt, -124.06pt)
– (450.21pt, -124.06pt)
– (318.43pt, -124.06pt) – cycle
[color_269680][even odd rule]
(450.22pt, -124.54pt) – (450.7pt, -124.54pt)
– (450.7pt, -124.54pt)
– (450.7pt, -124.06pt)
– (450.7pt, -124.06pt)
– (450.22pt, -124.06pt) – cycle
[color_269680][even odd rule]
(450.22pt, -124.54pt) – (450.7pt, -124.54pt)
– (450.7pt, -124.54pt)
– (450.7pt, -124.06pt)
– (450.7pt, -124.06pt)
– (450.22pt, -124.06pt) – cycle
[color_269680][even odd rule]
(317.95pt, -138.1pt) – (318.43pt, -138.1pt)
– (318.43pt, -138.1pt)
– (318.43pt, -137.62pt)
– (318.43pt, -137.62pt)
– (317.95pt, -137.62pt) – cycle
[color_269680][even odd rule]
(317.95pt, -138.1pt) – (318.43pt, -138.1pt)
– (318.43pt, -138.1pt)
– (318.43pt, -137.62pt)
– (318.43pt, -137.62pt)
– (317.95pt, -137.62pt) – cycle
[color_269680][even odd rule]
(318.43pt, -138.1pt) – (450.21pt, -138.1pt)
– (450.21pt, -138.1pt)
– (450.21pt, -137.62pt)
– (450.21pt, -137.62pt)
– (318.43pt, -137.62pt) – cycle
[color_269680][even odd rule]
(450.22pt, -138.1pt) – (450.7pt, -138.1pt)
– (450.7pt, -138.1pt)
– (450.7pt, -137.62pt)
– (450.7pt, -137.62pt)
– (450.22pt, -137.62pt) – cycle
[color_269680][even odd rule]
(450.22pt, -138.1pt) – (450.7pt, -138.1pt)
– (450.7pt, -138.1pt)
– (450.7pt, -137.62pt)
– (450.7pt, -137.62pt)
– (450.22pt, -137.62pt) – cycle
[color_269680][even odd rule]
(317.95pt, -137.62pt) – (318.43pt, -137.62pt)
– (318.43pt, -137.62pt)
– (318.43pt, -124.54pt)
– (318.43pt, -124.54pt)
– (317.95pt, -124.54pt) – cycle
[color_269680][even odd rule]
(450.22pt, -137.62pt) – (450.7pt, -137.62pt)
– (450.7pt, -137.62pt)
– (450.7pt, -124.54pt)
– (450.7pt, -124.54pt)
– (450.22pt, -124.54pt) – cycle
[color_269680][even odd rule]
(451.18pt, -138.46pt) – (451.66pt, -138.46pt)
– (451.66pt, -138.46pt)
– (451.66pt, -125.26pt)
– (451.66pt, -125.26pt)
– (451.18pt, -125.26pt) – cycle

(121.22pt, -151.54pt) – (178.004pt, -151.54pt)
– (178.004pt, -151.54pt)
– (178.004pt, -140.02pt)
– (178.004pt, -140.02pt)
– (121.22pt, -140.02pt) – cycle

(121.22pt, -151.54pt) – (178.004pt, -151.54pt)
– (178.004pt, -151.54pt)
– (178.004pt, -140.02pt)
– (178.004pt, -140.02pt)
– (121.22pt, -140.02pt) – cycle

[color_269680][even odd rule]
(119.78pt, -140.02pt) – (120.26pt, -140.02pt)
– (120.26pt, -140.02pt)
– (120.26pt, -138.46pt)
– (120.26pt, -138.46pt)
– (119.78pt, -138.46pt) – cycle
[color_269680][even odd rule]
(451.18pt, -140.02pt) – (451.66pt, -140.02pt)
– (451.66pt, -140.02pt)
– (451.66pt, -138.46pt)
– (451.66pt, -138.46pt)
– (451.18pt, -138.46pt) – cycle
[color_269680][even odd rule]
(120.74pt, -139.3pt) – (121.22pt, -139.3pt)
– (121.22pt, -139.3pt)
– (121.22pt, -138.82pt)
– (121.22pt, -138.82pt)
– (120.74pt, -138.82pt) – cycle
[color_269680][even odd rule]
(120.74pt, -139.3pt) – (121.22pt, -139.3pt)
– (121.22pt, -139.3pt)
– (121.22pt, -138.82pt)
– (121.22pt, -138.82pt)
– (120.74pt, -138.82pt) – cycle
[color_269680][even odd rule]
(121.22pt, -139.3pt) – (178.004pt, -139.3pt)
– (178.004pt, -139.3pt)
– (178.004pt, -138.82pt)
– (178.004pt, -138.82pt)
– (121.22pt, -138.82pt) – cycle
[color_269680][even odd rule]
(178.01pt, -139.3pt) – (178.49pt, -139.3pt)
– (178.49pt, -139.3pt)
– (178.49pt, -138.82pt)
– (178.49pt, -138.82pt)
– (178.01pt, -138.82pt) – cycle
[color_269680][even odd rule]
(178.01pt, -139.3pt) – (178.49pt, -139.3pt)
– (178.49pt, -139.3pt)
– (178.49pt, -138.82pt)
– (178.49pt, -138.82pt)
– (178.01pt, -138.82pt) – cycle
[color_269680][even odd rule]
(120.74pt, -152.74pt) – (121.22pt, -152.74pt)
– (121.22pt, -152.74pt)
– (121.22pt, -152.26pt)
– (121.22pt, -152.26pt)
– (120.74pt, -152.26pt) – cycle
[color_269680][even odd rule]
(120.74pt, -152.74pt) – (121.22pt, -152.74pt)
– (121.22pt, -152.74pt)
– (121.22pt, -152.26pt)
– (121.22pt, -152.26pt)
– (120.74pt, -152.26pt) – cycle
[color_269680][even odd rule]
(121.22pt, -152.74pt) – (178.004pt, -152.74pt)
– (178.004pt, -152.74pt)
– (178.004pt, -152.26pt)
– (178.004pt, -152.26pt)
– (121.22pt, -152.26pt) – cycle
[color_269680][even odd rule]
(178.01pt, -152.74pt) – (178.49pt, -152.74pt)
– (178.49pt, -152.74pt)
– (178.49pt, -152.26pt)
– (178.49pt, -152.26pt)
– (178.01pt, -152.26pt) – cycle
[color_269680][even odd rule]
(178.01pt, -152.74pt) – (178.49pt, -152.74pt)
– (178.49pt, -152.74pt)
– (178.49pt, -152.26pt)
– (178.49pt, -152.26pt)
– (178.01pt, -152.26pt) – cycle
[color_269680][even odd rule]
(120.74pt, -152.26pt) – (121.22pt, -152.26pt)
– (121.22pt, -152.26pt)
– (121.22pt, -139.3pt)
– (121.22pt, -139.3pt)
– (120.74pt, -139.3pt) – cycle
[color_269680][even odd rule]
(178.01pt, -152.26pt) – (178.49pt, -152.26pt)
– (178.49pt, -152.26pt)
– (178.49pt, -139.3pt)
– (178.49pt, -139.3pt)
– (178.01pt, -139.3pt) – cycle
[color_269680][even odd rule]
(119.78pt, -153.1pt) – (120.26pt, -153.1pt)
– (120.26pt, -153.1pt)
– (120.26pt, -140.02pt)
– (120.26pt, -140.02pt)
– (119.78pt, -140.02pt) – cycle
[color_269680][even odd rule]
(179.21pt, -139.3pt) – (179.69pt, -139.3pt)
– (179.69pt, -139.3pt)
– (179.69pt, -138.82pt)
– (179.69pt, -138.82pt)
– (179.21pt, -138.82pt) – cycle
[color_269680][even odd rule]
(179.21pt, -139.3pt) – (179.69pt, -139.3pt)
– (179.69pt, -139.3pt)
– (179.69pt, -138.82pt)
– (179.69pt, -138.82pt)
– (179.21pt, -138.82pt) – cycle
[color_269680][even odd rule]
(179.69pt, -139.3pt) – (316.75pt, -139.3pt)
– (316.75pt, -139.3pt)
– (316.75pt, -138.82pt)
– (316.75pt, -138.82pt)
– (179.69pt, -138.82pt) – cycle
[color_269680][even odd rule]
(316.75pt, -139.3pt) – (317.23pt, -139.3pt)
– (317.23pt, -139.3pt)
– (317.23pt, -138.82pt)
– (317.23pt, -138.82pt)
– (316.75pt, -138.82pt) – cycle
[color_269680][even odd rule]
(316.75pt, -139.3pt) – (317.23pt, -139.3pt)
– (317.23pt, -139.3pt)
– (317.23pt, -138.82pt)
– (317.23pt, -138.82pt)
– (316.75pt, -138.82pt) – cycle
[color_269680][even odd rule]
(179.21pt, -152.74pt) – (179.69pt, -152.74pt)
– (179.69pt, -152.74pt)
– (179.69pt, -152.26pt)
– (179.69pt, -152.26pt)
– (179.21pt, -152.26pt) – cycle
[color_269680][even odd rule]
(179.21pt, -152.74pt) – (179.69pt, -152.74pt)
– (179.69pt, -152.74pt)
– (179.69pt, -152.26pt)
– (179.69pt, -152.26pt)
– (179.21pt, -152.26pt) – cycle
[color_269680][even odd rule]
(179.69pt, -152.74pt) – (316.75pt, -152.74pt)
– (316.75pt, -152.74pt)
– (316.75pt, -152.26pt)
– (316.75pt, -152.26pt)
– (179.69pt, -152.26pt) – cycle
[color_269680][even odd rule]
(316.75pt, -152.74pt) – (317.23pt, -152.74pt)
– (317.23pt, -152.74pt)
– (317.23pt, -152.26pt)
– (317.23pt, -152.26pt)
– (316.75pt, -152.26pt) – cycle
[color_269680][even odd rule]
(316.75pt, -152.74pt) – (317.23pt, -152.74pt)
– (317.23pt, -152.74pt)
– (317.23pt, -152.26pt)
– (317.23pt, -152.26pt)
– (316.75pt, -152.26pt) – cycle
[color_269680][even odd rule]
(179.21pt, -152.26pt) – (179.69pt, -152.26pt)
– (179.69pt, -152.26pt)
– (179.69pt, -139.3pt)
– (179.69pt, -139.3pt)
– (179.21pt, -139.3pt) – cycle
[color_269680][even odd rule]
(316.75pt, -152.26pt) – (317.23pt, -152.26pt)
– (317.23pt, -152.26pt)
– (317.23pt, -139.3pt)
– (317.23pt, -139.3pt)
– (316.75pt, -139.3pt) – cycle
[color_269680][even odd rule]
(317.95pt, -139.3pt) – (318.43pt, -139.3pt)
– (318.43pt, -139.3pt)
– (318.43pt, -138.82pt)
– (318.43pt, -138.82pt)
– (317.95pt, -138.82pt) – cycle
[color_269680][even odd rule]
(317.95pt, -139.3pt) – (318.43pt, -139.3pt)
– (318.43pt, -139.3pt)
– (318.43pt, -138.82pt)
– (318.43pt, -138.82pt)
– (317.95pt, -138.82pt) – cycle
[color_269680][even odd rule]
(318.43pt, -139.3pt) – (450.21pt, -139.3pt)
– (450.21pt, -139.3pt)
– (450.21pt, -138.82pt)
– (450.21pt, -138.82pt)
– (318.43pt, -138.82pt) – cycle
[color_269680][even odd rule]
(450.22pt, -139.3pt) – (450.7pt, -139.3pt)
– (450.7pt, -139.3pt)
– (450.7pt, -138.82pt)
– (450.7pt, -138.82pt)
– (450.22pt, -138.82pt) – cycle
[color_269680][even odd rule]
(450.22pt, -139.3pt) – (450.7pt, -139.3pt)
– (450.7pt, -139.3pt)
– (450.7pt, -138.82pt)
– (450.7pt, -138.82pt)
– (450.22pt, -138.82pt) – cycle
[color_269680][even odd rule]
(317.95pt, -152.74pt) – (318.43pt, -152.74pt)
– (318.43pt, -152.74pt)
– (318.43pt, -152.26pt)
– (318.43pt, -152.26pt)
– (317.95pt, -152.26pt) – cycle
[color_269680][even odd rule]
(317.95pt, -152.74pt) – (318.43pt, -152.74pt)
– (318.43pt, -152.74pt)
– (318.43pt, -152.26pt)
– (318.43pt, -152.26pt)
– (317.95pt, -152.26pt) – cycle
[color_269680][even odd rule]
(318.43pt, -152.74pt) – (450.21pt, -152.74pt)
– (450.21pt, -152.74pt)
– (450.21pt, -152.26pt)
– (450.21pt, -152.26pt)
– (318.43pt, -152.26pt) – cycle
[color_269680][even odd rule]
(450.22pt, -152.74pt) – (450.7pt, -152.74pt)
– (450.7pt, -152.74pt)
– (450.7pt, -152.26pt)
– (450.7pt, -152.26pt)
– (450.22pt, -152.26pt) – cycle
[color_269680][even odd rule]
(450.22pt, -152.74pt) – (450.7pt, -152.74pt)
– (450.7pt, -152.74pt)
– (450.7pt, -152.26pt)
– (450.7pt, -152.26pt)
– (450.22pt, -152.26pt) – cycle
[color_269680][even odd rule]
(317.95pt, -152.26pt) – (318.43pt, -152.26pt)
– (318.43pt, -152.26pt)
– (318.43pt, -139.3pt)
– (318.43pt, -139.3pt)
– (317.95pt, -139.3pt) – cycle
[color_269680][even odd rule]
(450.22pt, -152.26pt) – (450.7pt, -152.26pt)
– (450.7pt, -152.26pt)
– (450.7pt, -139.3pt)
– (450.7pt, -139.3pt)
– (450.22pt, -139.3pt) – cycle
[color_269680][even odd rule]
(451.18pt, -153.1pt) – (451.66pt, -153.1pt)
– (451.66pt, -153.1pt)
– (451.66pt, -140.02pt)
– (451.66pt, -140.02pt)
– (451.18pt, -140.02pt) – cycle

(121.22pt, -166.18pt) – (178.004pt, -166.18pt)
– (178.004pt, -166.18pt)
– (178.004pt, -154.66pt)
– (178.004pt, -154.66pt)
– (121.22pt, -154.66pt) – cycle

(121.22pt, -166.18pt) – (178.004pt, -166.18pt)
– (178.004pt, -166.18pt)
– (178.004pt, -154.66pt)
– (178.004pt, -154.66pt)
– (121.22pt, -154.66pt) – cycle

[color_269680][even odd rule]
(119.78pt, -154.66pt) – (120.26pt, -154.66pt)
– (120.26pt, -154.66pt)
– (120.26pt, -153.1pt)
– (120.26pt, -153.1pt)
– (119.78pt, -153.1pt) – cycle
[color_269680][even odd rule]
(451.18pt, -154.66pt) – (451.66pt, -154.66pt)
– (451.66pt, -154.66pt)
– (451.66pt, -153.1pt)
– (451.66pt, -153.1pt)
– (451.18pt, -153.1pt) – cycle
[color_269680][even odd rule]
(120.74pt, -153.94pt) – (121.22pt, -153.94pt)
– (121.22pt, -153.94pt)
– (121.22pt, -153.46pt)
– (121.22pt, -153.46pt)
– (120.74pt, -153.46pt) – cycle
[color_269680][even odd rule]
(120.74pt, -153.94pt) – (121.22pt, -153.94pt)
– (121.22pt, -153.94pt)
– (121.22pt, -153.46pt)
– (121.22pt, -153.46pt)
– (120.74pt, -153.46pt) – cycle
[color_269680][even odd rule]
(121.22pt, -153.94pt) – (178.004pt, -153.94pt)
– (178.004pt, -153.94pt)
– (178.004pt, -153.46pt)
– (178.004pt, -153.46pt)
– (121.22pt, -153.46pt) – cycle
[color_269680][even odd rule]
(178.01pt, -153.94pt) – (178.49pt, -153.94pt)
– (178.49pt, -153.94pt)
– (178.49pt, -153.46pt)
– (178.49pt, -153.46pt)
– (178.01pt, -153.46pt) – cycle
[color_269680][even odd rule]
(178.01pt, -153.94pt) – (178.49pt, -153.94pt)
– (178.49pt, -153.94pt)
– (178.49pt, -153.46pt)
– (178.49pt, -153.46pt)
– (178.01pt, -153.46pt) – cycle
[color_269680][even odd rule]
(120.74pt, -167.5pt) – (121.22pt, -167.5pt)
– (121.22pt, -167.5pt)
– (121.22pt, -167.02pt)
– (121.22pt, -167.02pt)
– (120.74pt, -167.02pt) – cycle
[color_269680][even odd rule]
(120.74pt, -167.5pt) – (121.22pt, -167.5pt)
– (121.22pt, -167.5pt)
– (121.22pt, -167.02pt)
– (121.22pt, -167.02pt)
– (120.74pt, -167.02pt) – cycle
[color_269680][even odd rule]
(121.22pt, -167.5pt) – (178.004pt, -167.5pt)
– (178.004pt, -167.5pt)
– (178.004pt, -167.02pt)
– (178.004pt, -167.02pt)
– (121.22pt, -167.02pt) – cycle
[color_269680][even odd rule]
(178.01pt, -167.5pt) – (178.49pt, -167.5pt)
– (178.49pt, -167.5pt)
– (178.49pt, -167.02pt)
– (178.49pt, -167.02pt)
– (178.01pt, -167.02pt) – cycle
[color_269680][even odd rule]
(178.01pt, -167.5pt) – (178.49pt, -167.5pt)
– (178.49pt, -167.5pt)
– (178.49pt, -167.02pt)
– (178.49pt, -167.02pt)
– (178.01pt, -167.02pt) – cycle
[color_269680][even odd rule]
(120.74pt, -167.02pt) – (121.22pt, -167.02pt)
– (121.22pt, -167.02pt)
– (121.22pt, -153.94pt)
– (121.22pt, -153.94pt)
– (120.74pt, -153.94pt) – cycle
[color_269680][even odd rule]
(178.01pt, -167.02pt) – (178.49pt, -167.02pt)
– (178.49pt, -167.02pt)
– (178.49pt, -153.94pt)
– (178.49pt, -153.94pt)
– (178.01pt, -153.94pt) – cycle
[color_269680][even odd rule]
(119.78pt, -168.22pt) – (120.26pt, -168.22pt)
– (120.26pt, -168.22pt)
– (120.26pt, -154.66pt)
– (120.26pt, -154.66pt)
– (119.78pt, -154.66pt) – cycle
[color_269680][even odd rule]
(119.78pt, -168.7pt) – (120.26pt, -168.7pt)
– (120.26pt, -168.7pt)
– (120.26pt, -168.22pt)
– (120.26pt, -168.22pt)
– (119.78pt, -168.22pt) – cycle
[color_269680][even odd rule]
(119.78pt, -168.7pt) – (120.26pt, -168.7pt)
– (120.26pt, -168.7pt)
– (120.26pt, -168.22pt)
– (120.26pt, -168.22pt)
– (119.78pt, -168.22pt) – cycle
[color_269680][even odd rule]
(120.26pt, -168.7pt) – (178.844pt, -168.7pt)
– (178.844pt, -168.7pt)
– (178.844pt, -168.22pt)
– (178.844pt, -168.22pt)
– (120.26pt, -168.22pt) – cycle
[color_269680][even odd rule]
(179.21pt, -153.94pt) – (179.69pt, -153.94pt)
– (179.69pt, -153.94pt)
– (179.69pt, -153.46pt)
– (179.69pt, -153.46pt)
– (179.21pt, -153.46pt) – cycle
[color_269680][even odd rule]
(179.21pt, -153.94pt) – (179.69pt, -153.94pt)
– (179.69pt, -153.94pt)
– (179.69pt, -153.46pt)
– (179.69pt, -153.46pt)
– (179.21pt, -153.46pt) – cycle
[color_269680][even odd rule]
(179.69pt, -153.94pt) – (316.75pt, -153.94pt)
– (316.75pt, -153.94pt)
– (316.75pt, -153.46pt)
– (316.75pt, -153.46pt)
– (179.69pt, -153.46pt) – cycle
[color_269680][even odd rule]
(316.75pt, -153.94pt) – (317.23pt, -153.94pt)
– (317.23pt, -153.94pt)
– (317.23pt, -153.46pt)
– (317.23pt, -153.46pt)
– (316.75pt, -153.46pt) – cycle
[color_269680][even odd rule]
(316.75pt, -153.94pt) – (317.23pt, -153.94pt)
– (317.23pt, -153.94pt)
– (317.23pt, -153.46pt)
– (317.23pt, -153.46pt)
– (316.75pt, -153.46pt) – cycle
[color_269680][even odd rule]
(179.21pt, -167.5pt) – (179.69pt, -167.5pt)
– (179.69pt, -167.5pt)
– (179.69pt, -167.02pt)
– (179.69pt, -167.02pt)
– (179.21pt, -167.02pt) – cycle
[color_269680][even odd rule]
(179.21pt, -167.5pt) – (179.69pt, -167.5pt)
– (179.69pt, -167.5pt)
– (179.69pt, -167.02pt)
– (179.69pt, -167.02pt)
– (179.21pt, -167.02pt) – cycle
[color_269680][even odd rule]
(179.69pt, -167.5pt) – (316.75pt, -167.5pt)
– (316.75pt, -167.5pt)
– (316.75pt, -167.02pt)
– (316.75pt, -167.02pt)
– (179.69pt, -167.02pt) – cycle
[color_269680][even odd rule]
(316.75pt, -167.5pt) – (317.23pt, -167.5pt)
– (317.23pt, -167.5pt)
– (317.23pt, -167.02pt)
– (317.23pt, -167.02pt)
– (316.75pt, -167.02pt) – cycle
[color_269680][even odd rule]
(316.75pt, -167.5pt) – (317.23pt, -167.5pt)
– (317.23pt, -167.5pt)
– (317.23pt, -167.02pt)
– (317.23pt, -167.02pt)
– (316.75pt, -167.02pt) – cycle
[color_269680][even odd rule]
(179.21pt, -167.02pt) – (179.69pt, -167.02pt)
– (179.69pt, -167.02pt)
– (179.69pt, -153.94pt)
– (179.69pt, -153.94pt)
– (179.21pt, -153.94pt) – cycle
[color_269680][even odd rule]
(316.75pt, -167.02pt) – (317.23pt, -167.02pt)
– (317.23pt, -167.02pt)
– (317.23pt, -153.94pt)
– (317.23pt, -153.94pt)
– (316.75pt, -153.94pt) – cycle
[color_269680][even odd rule]
(178.85pt, -168.7pt) – (179.33pt, -168.7pt)
– (179.33pt, -168.7pt)
– (179.33pt, -168.22pt)
– (179.33pt, -168.22pt)
– (178.85pt, -168.22pt) – cycle
[color_269680][even odd rule]
(179.33pt, -168.7pt) – (317.59pt, -168.7pt)
– (317.59pt, -168.7pt)
– (317.59pt, -168.22pt)
– (317.59pt, -168.22pt)
– (179.33pt, -168.22pt) – cycle
[color_269680][even odd rule]
(317.95pt, -153.94pt) – (318.43pt, -153.94pt)
– (318.43pt, -153.94pt)
– (318.43pt, -153.46pt)
– (318.43pt, -153.46pt)
– (317.95pt, -153.46pt) – cycle
[color_269680][even odd rule]
(317.95pt, -153.94pt) – (318.43pt, -153.94pt)
– (318.43pt, -153.94pt)
– (318.43pt, -153.46pt)
– (318.43pt, -153.46pt)
– (317.95pt, -153.46pt) – cycle
[color_269680][even odd rule]
(318.43pt, -153.94pt) – (450.21pt, -153.94pt)
– (450.21pt, -153.94pt)
– (450.21pt, -153.46pt)
– (450.21pt, -153.46pt)
– (318.43pt, -153.46pt) – cycle
[color_269680][even odd rule]
(450.22pt, -153.94pt) – (450.7pt, -153.94pt)
– (450.7pt, -153.94pt)
– (450.7pt, -153.46pt)
– (450.7pt, -153.46pt)
– (450.22pt, -153.46pt) – cycle
[color_269680][even odd rule]
(450.22pt, -153.94pt) – (450.7pt, -153.94pt)
– (450.7pt, -153.94pt)
– (450.7pt, -153.46pt)
– (450.7pt, -153.46pt)
– (450.22pt, -153.46pt) – cycle
[color_269680][even odd rule]
(317.95pt, -167.5pt) – (318.43pt, -167.5pt)
– (318.43pt, -167.5pt)
– (318.43pt, -167.02pt)
– (318.43pt, -167.02pt)
– (317.95pt, -167.02pt) – cycle
[color_269680][even odd rule]
(317.95pt, -167.5pt) – (318.43pt, -167.5pt)
– (318.43pt, -167.5pt)
– (318.43pt, -167.02pt)
– (318.43pt, -167.02pt)
– (317.95pt, -167.02pt) – cycle
[color_269680][even odd rule]
(318.43pt, -167.5pt) – (450.21pt, -167.5pt)
– (450.21pt, -167.5pt)
– (450.21pt, -167.02pt)
– (450.21pt, -167.02pt)
– (318.43pt, -167.02pt) – cycle
[color_269680][even odd rule]
(450.22pt, -167.5pt) – (450.7pt, -167.5pt)
– (450.7pt, -167.5pt)
– (450.7pt, -167.02pt)
– (450.7pt, -167.02pt)
– (450.22pt, -167.02pt) – cycle
[color_269680][even odd rule]
(450.22pt, -167.5pt) – (450.7pt, -167.5pt)
– (450.7pt, -167.5pt)
– (450.7pt, -167.02pt)
– (450.7pt, -167.02pt)
– (450.22pt, -167.02pt) – cycle
[color_269680][even odd rule]
(317.95pt, -167.02pt) – (318.43pt, -167.02pt)
– (318.43pt, -167.02pt)
– (318.43pt, -153.94pt)
– (318.43pt, -153.94pt)
– (317.95pt, -153.94pt) – cycle
[color_269680][even odd rule]
(450.22pt, -167.02pt) – (450.7pt, -167.02pt)
– (450.7pt, -167.02pt)
– (450.7pt, -153.94pt)
– (450.7pt, -153.94pt)
– (450.22pt, -153.94pt) – cycle
[color_269680][even odd rule]
(317.59pt, -168.7pt) – (318.07pt, -168.7pt)
– (318.07pt, -168.7pt)
– (318.07pt, -168.22pt)
– (318.07pt, -168.22pt)
– (317.59pt, -168.22pt) – cycle
[color_269680][even odd rule]
(318.07pt, -168.7pt) – (451.17pt, -168.7pt)
– (451.17pt, -168.7pt)
– (451.17pt, -168.22pt)
– (451.17pt, -168.22pt)
– (318.07pt, -168.22pt) – cycle
[color_269680][even odd rule]
(451.18pt, -168.22pt) – (451.66pt, -168.22pt)
– (451.66pt, -168.22pt)
– (451.66pt, -154.66pt)
– (451.66pt, -154.66pt)
– (451.18pt, -154.66pt) – cycle
[color_269680][even odd rule]
(451.18pt, -168.7pt) – (451.66pt, -168.7pt)
– (451.66pt, -168.7pt)
– (451.66pt, -168.22pt)
– (451.66pt, -168.22pt)
– (451.18pt, -168.22pt) – cycle
[color_269680][even odd rule]
(451.18pt, -168.7pt) – (451.66pt, -168.7pt)
– (451.66pt, -168.7pt)
– (451.66pt, -168.22pt)
– (451.66pt, -168.22pt)
– (451.18pt, -168.22pt) – cycle

(57.024,-246.25)121T1cmrmnTo evaluate disambiguation performance, a confusion matrix comparing the predicted
(57.024,-262.09)121T1cmrmnand true labels of test entities was constructed and is displayed in Table 2.
(244.25,-292.93)9.961T1cmrmnPredicted Crater
(356.25,-292.93)9.961T1cmrmnPredicted Mission

[color_269680][even odd rule]
(119.78pt, -283.45pt) – (120.26pt, -283.45pt)
– (120.26pt, -283.45pt)
– (120.26pt, -281.05pt)
– (120.26pt, -281.05pt)
– (119.78pt, -281.05pt) – cycle
[color_269680][even odd rule]
(119.78pt, -281.53pt) – (120.26pt, -281.53pt)
– (120.26pt, -281.53pt)
– (120.26pt, -281.05pt)
– (120.26pt, -281.05pt)
– (119.78pt, -281.05pt) – cycle
[color_269680][even odd rule]
(120.26pt, -281.53pt) – (229.6pt, -281.53pt)
– (229.6pt, -281.53pt)
– (229.6pt, -281.05pt)
– (229.6pt, -281.05pt)
– (120.26pt, -281.05pt) – cycle
[color_269680][even odd rule]
(229.61pt, -281.53pt) – (230.09pt, -281.53pt)
– (230.09pt, -281.53pt)
– (230.09pt, -281.05pt)
– (230.09pt, -281.05pt)
– (229.61pt, -281.05pt) – cycle
[color_269680][even odd rule]
(230.09pt, -281.53pt) – (336.79pt, -281.53pt)
– (336.79pt, -281.53pt)
– (336.79pt, -281.05pt)
– (336.79pt, -281.05pt)
– (230.09pt, -281.05pt) – cycle
[color_269680][even odd rule]
(336.79pt, -281.53pt) – (337.27pt, -281.53pt)
– (337.27pt, -281.53pt)
– (337.27pt, -281.05pt)
– (337.27pt, -281.05pt)
– (336.79pt, -281.05pt) – cycle
[color_269680][even odd rule]
(337.27pt, -281.53pt) – (452.85pt, -281.53pt)
– (452.85pt, -281.53pt)
– (452.85pt, -281.05pt)
– (452.85pt, -281.05pt)
– (337.27pt, -281.05pt) – cycle
[color_269680][even odd rule]
(452.86pt, -283.45pt) – (453.34pt, -283.45pt)
– (453.34pt, -283.45pt)
– (453.34pt, -281.05pt)
– (453.34pt, -281.05pt)
– (452.86pt, -281.05pt) – cycle
[color_269680][even odd rule]
(452.86pt, -281.53pt) – (453.34pt, -281.53pt)
– (453.34pt, -281.53pt)
– (453.34pt, -281.05pt)
– (453.34pt, -281.05pt)
– (452.86pt, -281.05pt) – cycle
[color_269680][even odd rule]
(120.74pt, -282.73pt) – (121.22pt, -282.73pt)
– (121.22pt, -282.73pt)
– (121.22pt, -282.25pt)
– (121.22pt, -282.25pt)
– (120.74pt, -282.25pt) – cycle
[color_269680][even odd rule]
(120.74pt, -282.73pt) – (121.22pt, -282.73pt)
– (121.22pt, -282.73pt)
– (121.22pt, -282.25pt)
– (121.22pt, -282.25pt)
– (120.74pt, -282.25pt) – cycle
[color_269680][even odd rule]
(121.22pt, -282.73pt) – (228.76pt, -282.73pt)
– (228.76pt, -282.73pt)
– (228.76pt, -282.25pt)
– (228.76pt, -282.25pt)
– (121.22pt, -282.25pt) – cycle
[color_269680][even odd rule]
(228.77pt, -282.73pt) – (229.25pt, -282.73pt)
– (229.25pt, -282.73pt)
– (229.25pt, -282.25pt)
– (229.25pt, -282.25pt)
– (228.77pt, -282.25pt) – cycle
[color_269680][even odd rule]
(228.77pt, -282.73pt) – (229.25pt, -282.73pt)
– (229.25pt, -282.73pt)
– (229.25pt, -282.25pt)
– (229.25pt, -282.25pt)
– (228.77pt, -282.25pt) – cycle
[color_269680][even odd rule]
(120.74pt, -296.29pt) – (121.22pt, -296.29pt)
– (121.22pt, -296.29pt)
– (121.22pt, -295.81pt)
– (121.22pt, -295.81pt)
– (120.74pt, -295.81pt) – cycle
[color_269680][even odd rule]
(120.74pt, -296.29pt) – (121.22pt, -296.29pt)
– (121.22pt, -296.29pt)
– (121.22pt, -295.81pt)
– (121.22pt, -295.81pt)
– (120.74pt, -295.81pt) – cycle
[color_269680][even odd rule]
(121.22pt, -296.29pt) – (228.76pt, -296.29pt)
– (228.76pt, -296.29pt)
– (228.76pt, -295.81pt)
– (228.76pt, -295.81pt)
– (121.22pt, -295.81pt) – cycle
[color_269680][even odd rule]
(228.77pt, -296.29pt) – (229.25pt, -296.29pt)
– (229.25pt, -296.29pt)
– (229.25pt, -295.81pt)
– (229.25pt, -295.81pt)
– (228.77pt, -295.81pt) – cycle
[color_269680][even odd rule]
(228.77pt, -296.29pt) – (229.25pt, -296.29pt)
– (229.25pt, -296.29pt)
– (229.25pt, -295.81pt)
– (229.25pt, -295.81pt)
– (228.77pt, -295.81pt) – cycle
[color_269680][even odd rule]
(120.74pt, -295.81pt) – (121.22pt, -295.81pt)
– (121.22pt, -295.81pt)
– (121.22pt, -282.73pt)
– (121.22pt, -282.73pt)
– (120.74pt, -282.73pt) – cycle
[color_269680][even odd rule]
(228.77pt, -295.81pt) – (229.25pt, -295.81pt)
– (229.25pt, -295.81pt)
– (229.25pt, -282.73pt)
– (229.25pt, -282.73pt)
– (228.77pt, -282.73pt) – cycle
[color_269680][even odd rule]
(119.78pt, -296.65pt) – (120.26pt, -296.65pt)
– (120.26pt, -296.65pt)
– (120.26pt, -283.45pt)
– (120.26pt, -283.45pt)
– (119.78pt, -283.45pt) – cycle
[color_269680][even odd rule]
(229.97pt, -282.73pt) – (230.45pt, -282.73pt)
– (230.45pt, -282.73pt)
– (230.45pt, -282.25pt)
– (230.45pt, -282.25pt)
– (229.97pt, -282.25pt) – cycle
[color_269680][even odd rule]
(229.97pt, -282.73pt) – (230.45pt, -282.73pt)
– (230.45pt, -282.73pt)
– (230.45pt, -282.25pt)
– (230.45pt, -282.25pt)
– (229.97pt, -282.25pt) – cycle
[color_269680][even odd rule]
(230.45pt, -282.73pt) – (335.95pt, -282.73pt)
– (335.95pt, -282.73pt)
– (335.95pt, -282.25pt)
– (335.95pt, -282.25pt)
– (230.45pt, -282.25pt) – cycle
[color_269680][even odd rule]
(335.95pt, -282.73pt) – (336.43pt, -282.73pt)
– (336.43pt, -282.73pt)
– (336.43pt, -282.25pt)
– (336.43pt, -282.25pt)
– (335.95pt, -282.25pt) – cycle
[color_269680][even odd rule]
(335.95pt, -282.73pt) – (336.43pt, -282.73pt)
– (336.43pt, -282.73pt)
– (336.43pt, -282.25pt)
– (336.43pt, -282.25pt)
– (335.95pt, -282.25pt) – cycle
[color_269680][even odd rule]
(229.97pt, -296.29pt) – (230.45pt, -296.29pt)
– (230.45pt, -296.29pt)
– (230.45pt, -295.81pt)
– (230.45pt, -295.81pt)
– (229.97pt, -295.81pt) – cycle
[color_269680][even odd rule]
(229.97pt, -296.29pt) – (230.45pt, -296.29pt)
– (230.45pt, -296.29pt)
– (230.45pt, -295.81pt)
– (230.45pt, -295.81pt)
– (229.97pt, -295.81pt) – cycle
[color_269680][even odd rule]
(230.45pt, -296.29pt) – (335.95pt, -296.29pt)
– (335.95pt, -296.29pt)
– (335.95pt, -295.81pt)
– (335.95pt, -295.81pt)
– (230.45pt, -295.81pt) – cycle
[color_269680][even odd rule]
(335.95pt, -296.29pt) – (336.43pt, -296.29pt)
– (336.43pt, -296.29pt)
– (336.43pt, -295.81pt)
– (336.43pt, -295.81pt)
– (335.95pt, -295.81pt) – cycle
[color_269680][even odd rule]
(335.95pt, -296.29pt) – (336.43pt, -296.29pt)
– (336.43pt, -296.29pt)
– (336.43pt, -295.81pt)
– (336.43pt, -295.81pt)
– (335.95pt, -295.81pt) – cycle
[color_269680][even odd rule]
(229.97pt, -295.81pt) – (230.45pt, -295.81pt)
– (230.45pt, -295.81pt)
– (230.45pt, -282.73pt)
– (230.45pt, -282.73pt)
– (229.97pt, -282.73pt) – cycle
[color_269680][even odd rule]
(335.95pt, -295.81pt) – (336.43pt, -295.81pt)
– (336.43pt, -295.81pt)
– (336.43pt, -282.73pt)
– (336.43pt, -282.73pt)
– (335.95pt, -282.73pt) – cycle
[color_269680][even odd rule]
(337.27pt, -282.73pt) – (337.75pt, -282.73pt)
– (337.75pt, -282.73pt)
– (337.75pt, -282.25pt)
– (337.75pt, -282.25pt)
– (337.27pt, -282.25pt) – cycle
[color_269680][even odd rule]
(337.27pt, -282.73pt) – (337.75pt, -282.73pt)
– (337.75pt, -282.73pt)
– (337.75pt, -282.25pt)
– (337.75pt, -282.25pt)
– (337.27pt, -282.25pt) – cycle
[color_269680][even odd rule]
(337.75pt, -282.73pt) – (451.89pt, -282.73pt)
– (451.89pt, -282.73pt)
– (451.89pt, -282.25pt)
– (451.89pt, -282.25pt)
– (337.75pt, -282.25pt) – cycle
[color_269680][even odd rule]
(451.9pt, -282.73pt) – (452.38pt, -282.73pt)
– (452.38pt, -282.73pt)
– (452.38pt, -282.25pt)
– (452.38pt, -282.25pt)
– (451.9pt, -282.25pt) – cycle
[color_269680][even odd rule]
(451.9pt, -282.73pt) – (452.38pt, -282.73pt)
– (452.38pt, -282.73pt)
– (452.38pt, -282.25pt)
– (452.38pt, -282.25pt)
– (451.9pt, -282.25pt) – cycle
[color_269680][even odd rule]
(337.27pt, -296.29pt) – (337.75pt, -296.29pt)
– (337.75pt, -296.29pt)
– (337.75pt, -295.81pt)
– (337.75pt, -295.81pt)
– (337.27pt, -295.81pt) – cycle
[color_269680][even odd rule]
(337.27pt, -296.29pt) – (337.75pt, -296.29pt)
– (337.75pt, -296.29pt)
– (337.75pt, -295.81pt)
– (337.75pt, -295.81pt)
– (337.27pt, -295.81pt) – cycle
[color_269680][even odd rule]
(337.75pt, -296.29pt) – (451.89pt, -296.29pt)
– (451.89pt, -296.29pt)
– (451.89pt, -295.81pt)
– (451.89pt, -295.81pt)
– (337.75pt, -295.81pt) – cycle
[color_269680][even odd rule]
(451.9pt, -296.29pt) – (452.38pt, -296.29pt)
– (452.38pt, -296.29pt)
– (452.38pt, -295.81pt)
– (452.38pt, -295.81pt)
– (451.9pt, -295.81pt) – cycle
[color_269680][even odd rule]
(451.9pt, -296.29pt) – (452.38pt, -296.29pt)
– (452.38pt, -296.29pt)
– (452.38pt, -295.81pt)
– (452.38pt, -295.81pt)
– (451.9pt, -295.81pt) – cycle
[color_269680][even odd rule]
(337.27pt, -295.81pt) – (337.75pt, -295.81pt)
– (337.75pt, -295.81pt)
– (337.75pt, -282.73pt)
– (337.75pt, -282.73pt)
– (337.27pt, -282.73pt) – cycle
[color_269680][even odd rule]
(451.9pt, -295.81pt) – (452.38pt, -295.81pt)
– (452.38pt, -295.81pt)
– (452.38pt, -282.73pt)
– (452.38pt, -282.73pt)
– (451.9pt, -282.73pt) – cycle
[color_269680][even odd rule]
(452.86pt, -296.65pt) – (453.34pt, -296.65pt)
– (453.34pt, -296.65pt)
– (453.34pt, -283.45pt)
– (453.34pt, -283.45pt)
– (452.86pt, -283.45pt) – cycle

(121.22pt, -309.73pt) – (228.76pt, -309.73pt)
– (228.76pt, -309.73pt)
– (228.76pt, -298.21pt)
– (228.76pt, -298.21pt)
– (121.22pt, -298.21pt) – cycle

(121.22pt, -309.73pt) – (228.76pt, -309.73pt)
– (228.76pt, -309.73pt)
– (228.76pt, -298.21pt)
– (228.76pt, -298.21pt)
– (121.22pt, -298.21pt) – cycle

(141.94,-307.57)9.961T1cmrmnActual Crater

[color_269680][even odd rule]
(119.78pt, -298.21pt) – (120.26pt, -298.21pt)
– (120.26pt, -298.21pt)
– (120.26pt, -296.65pt)
– (120.26pt, -296.65pt)
– (119.78pt, -296.65pt) – cycle
[color_269680][even odd rule]
(452.86pt, -298.21pt) – (453.34pt, -298.21pt)
– (453.34pt, -298.21pt)
– (453.34pt, -296.65pt)
– (453.34pt, -296.65pt)
– (452.86pt, -296.65pt) – cycle
[color_269680][even odd rule]
(120.74pt, -297.49pt) – (121.22pt, -297.49pt)
– (121.22pt, -297.49pt)
– (121.22pt, -297.01pt)
– (121.22pt, -297.01pt)
– (120.74pt, -297.01pt) – cycle
[color_269680][even odd rule]
(120.74pt, -297.49pt) – (121.22pt, -297.49pt)
– (121.22pt, -297.49pt)
– (121.22pt, -297.01pt)
– (121.22pt, -297.01pt)
– (120.74pt, -297.01pt) – cycle
[color_269680][even odd rule]
(121.22pt, -297.49pt) – (228.76pt, -297.49pt)
– (228.76pt, -297.49pt)
– (228.76pt, -297.01pt)
– (228.76pt, -297.01pt)
– (121.22pt, -297.01pt) – cycle
[color_269680][even odd rule]
(228.77pt, -297.49pt) – (229.25pt, -297.49pt)
– (229.25pt, -297.49pt)
– (229.25pt, -297.01pt)
– (229.25pt, -297.01pt)
– (228.77pt, -297.01pt) – cycle
[color_269680][even odd rule]
(228.77pt, -297.49pt) – (229.25pt, -297.49pt)
– (229.25pt, -297.49pt)
– (229.25pt, -297.01pt)
– (229.25pt, -297.01pt)
– (228.77pt, -297.01pt) – cycle
[color_269680][even odd rule]
(120.74pt, -310.93pt) – (121.22pt, -310.93pt)
– (121.22pt, -310.93pt)
– (121.22pt, -310.45pt)
– (121.22pt, -310.45pt)
– (120.74pt, -310.45pt) – cycle
[color_269680][even odd rule]
(120.74pt, -310.93pt) – (121.22pt, -310.93pt)
– (121.22pt, -310.93pt)
– (121.22pt, -310.45pt)
– (121.22pt, -310.45pt)
– (120.74pt, -310.45pt) – cycle
[color_269680][even odd rule]
(121.22pt, -310.93pt) – (228.76pt, -310.93pt)
– (228.76pt, -310.93pt)
– (228.76pt, -310.45pt)
– (228.76pt, -310.45pt)
– (121.22pt, -310.45pt) – cycle
[color_269680][even odd rule]
(228.77pt, -310.93pt) – (229.25pt, -310.93pt)
– (229.25pt, -310.93pt)
– (229.25pt, -310.45pt)
– (229.25pt, -310.45pt)
– (228.77pt, -310.45pt) – cycle
[color_269680][even odd rule]
(228.77pt, -310.93pt) – (229.25pt, -310.93pt)
– (229.25pt, -310.93pt)
– (229.25pt, -310.45pt)
– (229.25pt, -310.45pt)
– (228.77pt, -310.45pt) – cycle
[color_269680][even odd rule]
(120.74pt, -310.45pt) – (121.22pt, -310.45pt)
– (121.22pt, -310.45pt)
– (121.22pt, -297.49pt)
– (121.22pt, -297.49pt)
– (120.74pt, -297.49pt) – cycle
[color_269680][even odd rule]
(228.77pt, -310.45pt) – (229.25pt, -310.45pt)
– (229.25pt, -310.45pt)
– (229.25pt, -297.49pt)
– (229.25pt, -297.49pt)
– (228.77pt, -297.49pt) – cycle
[color_269680][even odd rule]
(119.78pt, -311.29pt) – (120.26pt, -311.29pt)
– (120.26pt, -311.29pt)
– (120.26pt, -298.21pt)
– (120.26pt, -298.21pt)
– (119.78pt, -298.21pt) – cycle
[color_269680][even odd rule]
(229.97pt, -297.49pt) – (230.45pt, -297.49pt)
– (230.45pt, -297.49pt)
– (230.45pt, -297.01pt)
– (230.45pt, -297.01pt)
– (229.97pt, -297.01pt) – cycle
[color_269680][even odd rule]
(229.97pt, -297.49pt) – (230.45pt, -297.49pt)
– (230.45pt, -297.49pt)
– (230.45pt, -297.01pt)
– (230.45pt, -297.01pt)
– (229.97pt, -297.01pt) – cycle
[color_269680][even odd rule]
(230.45pt, -297.49pt) – (335.95pt, -297.49pt)
– (335.95pt, -297.49pt)
– (335.95pt, -297.01pt)
– (335.95pt, -297.01pt)
– (230.45pt, -297.01pt) – cycle
[color_269680][even odd rule]
(335.95pt, -297.49pt) – (336.43pt, -297.49pt)
– (336.43pt, -297.49pt)
– (336.43pt, -297.01pt)
– (336.43pt, -297.01pt)
– (335.95pt, -297.01pt) – cycle
[color_269680][even odd rule]
(335.95pt, -297.49pt) – (336.43pt, -297.49pt)
– (336.43pt, -297.49pt)
– (336.43pt, -297.01pt)
– (336.43pt, -297.01pt)
– (335.95pt, -297.01pt) – cycle
[color_269680][even odd rule]
(229.97pt, -310.93pt) – (230.45pt, -310.93pt)
– (230.45pt, -310.93pt)
– (230.45pt, -310.45pt)
– (230.45pt, -310.45pt)
– (229.97pt, -310.45pt) – cycle
[color_269680][even odd rule]
(229.97pt, -310.93pt) – (230.45pt, -310.93pt)
– (230.45pt, -310.93pt)
– (230.45pt, -310.45pt)
– (230.45pt, -310.45pt)
– (229.97pt, -310.45pt) – cycle
[color_269680][even odd rule]
(230.45pt, -310.93pt) – (335.95pt, -310.93pt)
– (335.95pt, -310.93pt)
– (335.95pt, -310.45pt)
– (335.95pt, -310.45pt)
– (230.45pt, -310.45pt) – cycle
[color_269680][even odd rule]
(335.95pt, -310.93pt) – (336.43pt, -310.93pt)
– (336.43pt, -310.93pt)
– (336.43pt, -310.45pt)
– (336.43pt, -310.45pt)
– (335.95pt, -310.45pt) – cycle
[color_269680][even odd rule]
(335.95pt, -310.93pt) – (336.43pt, -310.93pt)
– (336.43pt, -310.93pt)
– (336.43pt, -310.45pt)
– (336.43pt, -310.45pt)
– (335.95pt, -310.45pt) – cycle
[color_269680][even odd rule]
(229.97pt, -310.45pt) – (230.45pt, -310.45pt)
– (230.45pt, -310.45pt)
– (230.45pt, -297.49pt)
– (230.45pt, -297.49pt)
– (229.97pt, -297.49pt) – cycle
[color_269680][even odd rule]
(335.95pt, -310.45pt) – (336.43pt, -310.45pt)
– (336.43pt, -310.45pt)
– (336.43pt, -297.49pt)
– (336.43pt, -297.49pt)
– (335.95pt, -297.49pt) – cycle
[color_269680][even odd rule]
(337.27pt, -297.49pt) – (337.75pt, -297.49pt)
– (337.75pt, -297.49pt)
– (337.75pt, -297.01pt)
– (337.75pt, -297.01pt)
– (337.27pt, -297.01pt) – cycle
[color_269680][even odd rule]
(337.27pt, -297.49pt) – (337.75pt, -297.49pt)
– (337.75pt, -297.49pt)
– (337.75pt, -297.01pt)
– (337.75pt, -297.01pt)
– (337.27pt, -297.01pt) – cycle
[color_269680][even odd rule]
(337.75pt, -297.49pt) – (451.89pt, -297.49pt)
– (451.89pt, -297.49pt)
– (451.89pt, -297.01pt)
– (451.89pt, -297.01pt)
– (337.75pt, -297.01pt) – cycle
[color_269680][even odd rule]
(451.9pt, -297.49pt) – (452.38pt, -297.49pt)
– (452.38pt, -297.49pt)
– (452.38pt, -297.01pt)
– (452.38pt, -297.01pt)
– (451.9pt, -297.01pt) – cycle
[color_269680][even odd rule]
(451.9pt, -297.49pt) – (452.38pt, -297.49pt)
– (452.38pt, -297.49pt)
– (452.38pt, -297.01pt)
– (452.38pt, -297.01pt)
– (451.9pt, -297.01pt) – cycle
[color_269680][even odd rule]
(337.27pt, -310.93pt) – (337.75pt, -310.93pt)
– (337.75pt, -310.93pt)
– (337.75pt, -310.45pt)
– (337.75pt, -310.45pt)
– (337.27pt, -310.45pt) – cycle
[color_269680][even odd rule]
(337.27pt, -310.93pt) – (337.75pt, -310.93pt)
– (337.75pt, -310.93pt)
– (337.75pt, -310.45pt)
– (337.75pt, -310.45pt)
– (337.27pt, -310.45pt) – cycle
[color_269680][even odd rule]
(337.75pt, -310.93pt) – (451.89pt, -310.93pt)
– (451.89pt, -310.93pt)
– (451.89pt, -310.45pt)
– (451.89pt, -310.45pt)
– (337.75pt, -310.45pt) – cycle
[color_269680][even odd rule]
(451.9pt, -310.93pt) – (452.38pt, -310.93pt)
– (452.38pt, -310.93pt)
– (452.38pt, -310.45pt)
– (452.38pt, -310.45pt)
– (451.9pt, -310.45pt) – cycle
[color_269680][even odd rule]
(451.9pt, -310.93pt) – (452.38pt, -310.93pt)
– (452.38pt, -310.93pt)
– (452.38pt, -310.45pt)
– (452.38pt, -310.45pt)
– (451.9pt, -310.45pt) – cycle
[color_269680][even odd rule]
(337.27pt, -310.45pt) – (337.75pt, -310.45pt)
– (337.75pt, -310.45pt)
– (337.75pt, -297.49pt)
– (337.75pt, -297.49pt)
– (337.27pt, -297.49pt) – cycle
[color_269680][even odd rule]
(451.9pt, -310.45pt) – (452.38pt, -310.45pt)
– (452.38pt, -310.45pt)
– (452.38pt, -297.49pt)
– (452.38pt, -297.49pt)
– (451.9pt, -297.49pt) – cycle
[color_269680][even odd rule]
(452.86pt, -311.29pt) – (453.34pt, -311.29pt)
– (453.34pt, -311.29pt)
– (453.34pt, -298.21pt)
– (453.34pt, -298.21pt)
– (452.86pt, -298.21pt) – cycle

(121.22pt, -324.49pt) – (228.76pt, -324.49pt)
– (228.76pt, -324.49pt)
– (228.76pt, -312.97pt)
– (228.76pt, -312.97pt)
– (121.22pt, -312.97pt) – cycle

(121.22pt, -324.49pt) – (228.76pt, -324.49pt)
– (228.76pt, -324.49pt)
– (228.76pt, -312.97pt)
– (228.76pt, -312.97pt)
– (121.22pt, -312.97pt) – cycle

(141.94,-322.33)9.961T1cmrmnActual Mission

[color_269680][even odd rule]
(119.78pt, -312.85pt) – (120.26pt, -312.85pt)
– (120.26pt, -312.85pt)
– (120.26pt, -311.29pt)
– (120.26pt, -311.29pt)
– (119.78pt, -311.29pt) – cycle
[color_269680][even odd rule]
(452.86pt, -312.85pt) – (453.34pt, -312.85pt)
– (453.34pt, -312.85pt)
– (453.34pt, -311.29pt)
– (453.34pt, -311.29pt)
– (452.86pt, -311.29pt) – cycle
[color_269680][even odd rule]
(120.74pt, -312.13pt) – (121.22pt, -312.13pt)
– (121.22pt, -312.13pt)
– (121.22pt, -311.65pt)
– (121.22pt, -311.65pt)
– (120.74pt, -311.65pt) – cycle
[color_269680][even odd rule]
(120.74pt, -312.13pt) – (121.22pt, -312.13pt)
– (121.22pt, -312.13pt)
– (121.22pt, -311.65pt)
– (121.22pt, -311.65pt)
– (120.74pt, -311.65pt) – cycle
[color_269680][even odd rule]
(121.22pt, -312.13pt) – (228.76pt, -312.13pt)
– (228.76pt, -312.13pt)
– (228.76pt, -311.65pt)
– (228.76pt, -311.65pt)
– (121.22pt, -311.65pt) – cycle
[color_269680][even odd rule]
(228.77pt, -312.13pt) – (229.25pt, -312.13pt)
– (229.25pt, -312.13pt)
– (229.25pt, -311.65pt)
– (229.25pt, -311.65pt)
– (228.77pt, -311.65pt) – cycle
[color_269680][even odd rule]
(228.77pt, -312.13pt) – (229.25pt, -312.13pt)
– (229.25pt, -312.13pt)
– (229.25pt, -311.65pt)
– (229.25pt, -311.65pt)
– (228.77pt, -311.65pt) – cycle
[color_269680][even odd rule]
(120.74pt, -325.69pt) – (121.22pt, -325.69pt)
– (121.22pt, -325.69pt)
– (121.22pt, -325.21pt)
– (121.22pt, -325.21pt)
– (120.74pt, -325.21pt) – cycle
[color_269680][even odd rule]
(120.74pt, -325.69pt) – (121.22pt, -325.69pt)
– (121.22pt, -325.69pt)
– (121.22pt, -325.21pt)
– (121.22pt, -325.21pt)
– (120.74pt, -325.21pt) – cycle
[color_269680][even odd rule]
(121.22pt, -325.69pt) – (228.76pt, -325.69pt)
– (228.76pt, -325.69pt)
– (228.76pt, -325.21pt)
– (228.76pt, -325.21pt)
– (121.22pt, -325.21pt) – cycle
[color_269680][even odd rule]
(228.77pt, -325.69pt) – (229.25pt, -325.69pt)
– (229.25pt, -325.69pt)
– (229.25pt, -325.21pt)
– (229.25pt, -325.21pt)
– (228.77pt, -325.21pt) – cycle
[color_269680][even odd rule]
(228.77pt, -325.69pt) – (229.25pt, -325.69pt)
– (229.25pt, -325.69pt)
– (229.25pt, -325.21pt)
– (229.25pt, -325.21pt)
– (228.77pt, -325.21pt) – cycle
[color_269680][even odd rule]
(120.74pt, -325.21pt) – (121.22pt, -325.21pt)
– (121.22pt, -325.21pt)
– (121.22pt, -312.13pt)
– (121.22pt, -312.13pt)
– (120.74pt, -312.13pt) – cycle
[color_269680][even odd rule]
(228.77pt, -325.21pt) – (229.25pt, -325.21pt)
– (229.25pt, -325.21pt)
– (229.25pt, -312.13pt)
– (229.25pt, -312.13pt)
– (228.77pt, -312.13pt) – cycle
[color_269680][even odd rule]
(119.78pt, -326.41pt) – (120.26pt, -326.41pt)
– (120.26pt, -326.41pt)
– (120.26pt, -312.85pt)
– (120.26pt, -312.85pt)
– (119.78pt, -312.85pt) – cycle
[color_269680][even odd rule]
(119.78pt, -326.89pt) – (120.26pt, -326.89pt)
– (120.26pt, -326.89pt)
– (120.26pt, -326.41pt)
– (120.26pt, -326.41pt)
– (119.78pt, -326.41pt) – cycle
[color_269680][even odd rule]
(119.78pt, -326.89pt) – (120.26pt, -326.89pt)
– (120.26pt, -326.89pt)
– (120.26pt, -326.41pt)
– (120.26pt, -326.41pt)
– (119.78pt, -326.41pt) – cycle
[color_269680][even odd rule]
(120.26pt, -326.89pt) – (229.6pt, -326.89pt)
– (229.6pt, -326.89pt)
– (229.6pt, -326.41pt)
– (229.6pt, -326.41pt)
– (120.26pt, -326.41pt) – cycle
[color_269680][even odd rule]
(229.97pt, -312.13pt) – (230.45pt, -312.13pt)
– (230.45pt, -312.13pt)
– (230.45pt, -311.65pt)
– (230.45pt, -311.65pt)
– (229.97pt, -311.65pt) – cycle
[color_269680][even odd rule]
(229.97pt, -312.13pt) – (230.45pt, -312.13pt)
– (230.45pt, -312.13pt)
– (230.45pt, -311.65pt)
– (230.45pt, -311.65pt)
– (229.97pt, -311.65pt) – cycle
[color_269680][even odd rule]
(230.45pt, -312.13pt) – (335.95pt, -312.13pt)
– (335.95pt, -312.13pt)
– (335.95pt, -311.65pt)
– (335.95pt, -311.65pt)
– (230.45pt, -311.65pt) – cycle
[color_269680][even odd rule]
(335.95pt, -312.13pt) – (336.43pt, -312.13pt)
– (336.43pt, -312.13pt)
– (336.43pt, -311.65pt)
– (336.43pt, -311.65pt)
– (335.95pt, -311.65pt) – cycle
[color_269680][even odd rule]
(335.95pt, -312.13pt) – (336.43pt, -312.13pt)
– (336.43pt, -312.13pt)
– (336.43pt, -311.65pt)
– (336.43pt, -311.65pt)
– (335.95pt, -311.65pt) – cycle
[color_269680][even odd rule]
(229.97pt, -325.69pt) – (230.45pt, -325.69pt)
– (230.45pt, -325.69pt)
– (230.45pt, -325.21pt)
– (230.45pt, -325.21pt)
– (229.97pt, -325.21pt) – cycle
[color_269680][even odd rule]
(229.97pt, -325.69pt) – (230.45pt, -325.69pt)
– (230.45pt, -325.69pt)
– (230.45pt, -325.21pt)
– (230.45pt, -325.21pt)
– (229.97pt, -325.21pt) – cycle
[color_269680][even odd rule]
(230.45pt, -325.69pt) – (335.95pt, -325.69pt)
– (335.95pt, -325.69pt)
– (335.95pt, -325.21pt)
– (335.95pt, -325.21pt)
– (230.45pt, -325.21pt) – cycle
[color_269680][even odd rule]
(335.95pt, -325.69pt) – (336.43pt, -325.69pt)
– (336.43pt, -325.69pt)
– (336.43pt, -325.21pt)
– (336.43pt, -325.21pt)
– (335.95pt, -325.21pt) – cycle
[color_269680][even odd rule]
(335.95pt, -325.69pt) – (336.43pt, -325.69pt)
– (336.43pt, -325.69pt)
– (336.43pt, -325.21pt)
– (336.43pt, -325.21pt)
– (335.95pt, -325.21pt) – cycle
[color_269680][even odd rule]
(229.97pt, -325.21pt) – (230.45pt, -325.21pt)
– (230.45pt, -325.21pt)
– (230.45pt, -312.13pt)
– (230.45pt, -312.13pt)
– (229.97pt, -312.13pt) – cycle
[color_269680][even odd rule]
(335.95pt, -325.21pt) – (336.43pt, -325.21pt)
– (336.43pt, -325.21pt)
– (336.43pt, -312.13pt)
– (336.43pt, -312.13pt)
– (335.95pt, -312.13pt) – cycle
[color_269680][even odd rule]
(229.61pt, -326.89pt) – (230.09pt, -326.89pt)
– (230.09pt, -326.89pt)
– (230.09pt, -326.41pt)
– (230.09pt, -326.41pt)
– (229.61pt, -326.41pt) – cycle
[color_269680][even odd rule]
(230.09pt, -326.89pt) – (336.79pt, -326.89pt)
– (336.79pt, -326.89pt)
– (336.79pt, -326.41pt)
– (336.79pt, -326.41pt)
– (230.09pt, -326.41pt) – cycle
[color_269680][even odd rule]
(337.27pt, -312.13pt) – (337.75pt, -312.13pt)
– (337.75pt, -312.13pt)
– (337.75pt, -311.65pt)
– (337.75pt, -311.65pt)
– (337.27pt, -311.65pt) – cycle
[color_269680][even odd rule]
(337.27pt, -312.13pt) – (337.75pt, -312.13pt)
– (337.75pt, -312.13pt)
– (337.75pt, -311.65pt)
– (337.75pt, -311.65pt)
– (337.27pt, -311.65pt) – cycle
[color_269680][even odd rule]
(337.75pt, -312.13pt) – (451.89pt, -312.13pt)
– (451.89pt, -312.13pt)
– (451.89pt, -311.65pt)
– (451.89pt, -311.65pt)
– (337.75pt, -311.65pt) – cycle
[color_269680][even odd rule]
(451.9pt, -312.13pt) – (452.38pt, -312.13pt)
– (452.38pt, -312.13pt)
– (452.38pt, -311.65pt)
– (452.38pt, -311.65pt)
– (451.9pt, -311.65pt) – cycle
[color_269680][even odd rule]
(451.9pt, -312.13pt) – (452.38pt, -312.13pt)
– (452.38pt, -312.13pt)
– (452.38pt, -311.65pt)
– (452.38pt, -311.65pt)
– (451.9pt, -311.65pt) – cycle
[color_269680][even odd rule]
(337.27pt, -325.69pt) – (337.75pt, -325.69pt)
– (337.75pt, -325.69pt)
– (337.75pt, -325.21pt)
– (337.75pt, -325.21pt)
– (337.27pt, -325.21pt) – cycle
[color_269680][even odd rule]
(337.27pt, -325.69pt) – (337.75pt, -325.69pt)
– (337.75pt, -325.69pt)
– (337.75pt, -325.21pt)
– (337.75pt, -325.21pt)
– (337.27pt, -325.21pt) – cycle
[color_269680][even odd rule]
(337.75pt, -325.69pt) – (451.89pt, -325.69pt)
– (451.89pt, -325.69pt)
– (451.89pt, -325.21pt)
– (451.89pt, -325.21pt)
– (337.75pt, -325.21pt) – cycle
[color_269680][even odd rule]
(451.9pt, -325.69pt) – (452.38pt, -325.69pt)
– (452.38pt, -325.69pt)
– (452.38pt, -325.21pt)
– (452.38pt, -325.21pt)
– (451.9pt, -325.21pt) – cycle
[color_269680][even odd rule]
(451.9pt, -325.69pt) – (452.38pt, -325.69pt)
– (452.38pt, -325.69pt)
– (452.38pt, -325.21pt)
– (452.38pt, -325.21pt)
– (451.9pt, -325.21pt) – cycle
[color_269680][even odd rule]
(337.27pt, -325.21pt) – (337.75pt, -325.21pt)
– (337.75pt, -325.21pt)
– (337.75pt, -312.13pt)
– (337.75pt, -312.13pt)
– (337.27pt, -312.13pt) – cycle
[color_269680][even odd rule]
(451.9pt, -325.21pt) – (452.38pt, -325.21pt)
– (452.38pt, -325.21pt)
– (452.38pt, -312.13pt)
– (452.38pt, -312.13pt)
– (451.9pt, -312.13pt) – cycle
[color_269680][even odd rule]
(336.79pt, -326.89pt) – (337.27pt, -326.89pt)
– (337.27pt, -326.89pt)
– (337.27pt, -326.41pt)
– (337.27pt, -326.41pt)
– (336.79pt, -326.41pt) – cycle
[color_269680][even odd rule]
(337.27pt, -326.89pt) – (452.85pt, -326.89pt)
– (452.85pt, -326.89pt)
– (452.85pt, -326.41pt)
– (452.85pt, -326.41pt)
– (337.27pt, -326.41pt) – cycle
[color_269680][even odd rule]
(452.86pt, -326.41pt) – (453.34pt, -326.41pt)
– (453.34pt, -326.41pt)
– (453.34pt, -312.85pt)
– (453.34pt, -312.85pt)
– (452.86pt, -312.85pt) – cycle
[color_269680][even odd rule]
(452.86pt, -326.89pt) – (453.34pt, -326.89pt)
– (453.34pt, -326.89pt)
– (453.34pt, -326.41pt)
– (453.34pt, -326.41pt)
– (452.86pt, -326.41pt) – cycle
[color_269680][even odd rule]
(452.86pt, -326.89pt) – (453.34pt, -326.89pt)
– (453.34pt, -326.89pt)
– (453.34pt, -326.41pt)
– (453.34pt, -326.41pt)
– (452.86pt, -326.41pt) – cycle

(57.024,-392.41)121T1cmrmnSeveral evaluation metrics were calculated. The overall accuracy was 69.76%. For the
(57.024,-408.27)121T1cmrmncrater class, precision was 74.77%, recall was 79.82%, and F1 score was 77.21%. For
(57.024,-424.23)121T1cmrmnthe mission class, precision was 58.78%, recall was 51.67%, and F1 score was 55.05%.
(57.024,-440.07)121T1cmrmnThis indicates better performance in predicting crater instances compared to mission
(57.024,-486.15)121T1cmrmnThe results highlight the effectiveness of the approach in disambiguating entities and
(57.024,-502.11)121T1cmrmnunderstanding their contextual relationships in astronomy. The clustering algorithm
(57.024,-517.95)121T1cmrmnsuccessfully grouped entities based on their associations with Apollo craters and
(57.024,-533.79)121T1cmrmnmissions, providing a clear separation between the two contexts. The evaluation metrics
(57.024,-549.63)121T1cmrmnfurther validate the model's performance, particularly in identifying and classifying
(57.024,-565.59)121T1cmrmncrater-related entities. However, the lower performance for mission-related entities
(57.024,-581.46)121T1cmrmnsuggests potential for improvement in capturing their complexities.
(57.024,-627.3)121T1cmrmn6. Conclusion
(57.024,-657.54)121T1cmrmnThis study demonstrates an approach to entity disambiguation and contextual
(57.024,-673.5)121T1cmrmnunderstanding in the astronomical domain by leveraging large language models and
(57.024,-689.336)121T1cmrmnknowledge graph clustering techniques. The combination of the GPT-4 language model
(57.024,-705.176)121T1cmrmnfor entity and relationship extraction with the Leiden algorithm for community detection

[color_283006][even odd rule]
(93.5pt, -219.58pt) – (496pt, -219.58pt)
– (496pt, -219.58pt)
– (496pt, -169.98pt)
– (496pt, -169.98pt)
– (93.5pt, -169.98pt) – cycle

(93.74pt, -215.74pt) – (495.81pt, -215.74pt)
– (495.81pt, -215.74pt)
– (495.81pt, -173.86pt)
– (495.81pt, -173.86pt)
– (93.74pt, -173.86pt) – cycle

(93.74pt, -215.74pt) – (495.81pt, -215.74pt)
– (495.81pt, -215.74pt)
– (495.81pt, -173.86pt)
– (495.81pt, -173.86pt)
– (93.74pt, -173.86pt) – cycle

(100.94,-184.3)11.041T1cmrmnTable 1: Distribution of crater and mission entities within each cluster identified by the
(100.94,-198.82)11.041T1cmrmnclustering algorithm, showing that Cluster 0 is predominantly associated with mission
(100.94,-213.34)11.041T1cmrmnentities, while Clusters 1 and 2 are more strongly associated with crater entities.

[color_283006][even odd rule]
(91.5pt, -366.49pt) – (490.2pt, -366.49pt)
– (490.2pt, -366.49pt)
– (490.2pt, -327.24pt)
– (490.2pt, -327.24pt)
– (91.5pt, -327.24pt) – cycle

(91.82pt, -362.65pt) – (490.05pt, -362.65pt)
– (490.05pt, -362.65pt)
– (490.05pt, -331.21pt)
– (490.05pt, -331.21pt)
– (91.82pt, -331.21pt) – cycle

(91.82pt, -362.65pt) – (490.05pt, -362.65pt)
– (490.05pt, -362.65pt)
– (490.05pt, -331.21pt)
– (490.05pt, -331.21pt)
– (91.82pt, -331.21pt) – cycle

(99.02,-341.65)11.041T1cmrmnTable 2: Confusion matrix evaluating the performance of the entity disambiguation
(99.02,-356.05)11.041T1cmrmnapproach, showing the predicted versus actual labels for crater and mission entities.

(57.024,-73.29999)121T1cmrmnsuccessfully disambiguates entities related to Apollo craters and Apollo missions in
(57.024,-89.14001)121T1cmrmnastronomical text data.
(57.024,-119.38)121T1cmrmnThe experimental results highlight the effectiveness of the approach, with the clustering
(57.024,-135.34)121T1cmrmnalgorithm identifying distinct clusters associated with crater and mission entities.
(57.024,-151.18)121T1cmrmnEvaluation metrics demonstrate the model's commendable performance in
(57.024,-167.02)121T1cmrmndisambiguating and classifying crater-related entities, while acknowledging the potential
(57.024,-182.86)121T1cmrmnfor improvement in capturing the complexities of mission-related entities.
(57.024,-213.22)121T1cmrmnThis work contributes to the growing body of research on applying advanced natural
(57.024,-229.06)121T1cmrmnlanguage processing and knowledge graph-based approaches in astronomy. The
(57.024,-244.93)121T1cmrmnproposed methodology offers a promising direction for improving information retrieval,
(57.024,-260.77)121T1cmrmndata integration, and knowledge organization in astronomical text data, ultimately
(57.024,-276.73)121T1cmrmnleading to deeper insights and accelerated scientific progress.
(57.024,-360.13)121T1cmrmn1. Abu-Rasheed, H., Weber, C., & Fathi, M. (2024). Knowledge Graphs as Context
(75.024,-375.97)121T1cmrmnSources for LLM-Based Explanations of Learning Recommendations. arXiv preprint
(75.024,-391.81)121T1cmrmnarXiv:2403.03008. https://doi.org/10.48550/arXiv.2403.03008.

[color_30046][even odd rule]
(175.85pt, -393.97pt) – (399.43pt, -393.97pt)
– (399.43pt, -393.97pt)
– (399.43pt, -393.13pt)
– (399.43pt, -393.13pt)
– (175.85pt, -393.13pt) – cycle

(57.024,-407.79)121T1cmrmn2. Brescia, M., & Longo, G. (2012). Astroinformatics, data mining and the future of
(75.024,-423.63)121T1cmrmnastronomical research. Nuclear Instruments and Methods in Physics Research
(75.024,-439.47)121T1cmrmnSection A: Accelerators, Spectrometers, Detectors and Associated Equipment, 728,
(75.024,-455.31)121T1cmrmn35-41. https://doi.org/10.1016/j.nima.2012.12.027.

[color_269680][even odd rule]
(109.1pt, -457.47pt) – (112.46pt, -457.47pt)
– (112.46pt, -457.47pt)
– (112.46pt, -456.63pt)
– (112.46pt, -456.63pt)
– (109.1pt, -456.63pt) – cycle
[color_30046][even odd rule]
(112.46pt, -457.47pt) – (337.51pt, -457.47pt)
– (337.51pt, -457.47pt)
– (337.51pt, -456.63pt)
– (337.51pt, -456.63pt)
– (112.46pt, -456.63pt) – cycle

(57.024,-471.27)121T1cmrmn3. Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., & Larson,
(75.024,-487.11)121T1cmrmnJ. (2024). From Local to Global: A Graph RAG Approach to Query-Focused
(75.024,-502.95)121T1cmrmnSummarization. arXiv preprint arXiv:2404.16130.

[color_30046][even odd rule]
(75.024pt, -520.95pt) – (298.604pt, -520.95pt)
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– (298.604pt, -520.11pt)
– (298.604pt, -520.11pt)
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(57.024,-534.63)121T1cmrmn4. Jia, R., Zhang, B., Rodríguez Méndez, S. J., & Omran, P. G. (2024). Leveraging
(75.024,-550.59)121T1cmrmnLarge Language Models for Semantic Query Processing in a Scholarly Knowledge
(75.024,-566.43)121T1cmrmnGraph. arXiv preprint arXiv:2405.15374. https://doi.org/10.48550/arXiv.2405.15374.

[color_30046][even odd rule]
(290.69pt, -568.59pt) – (514.18pt, -568.59pt)
– (514.18pt, -568.59pt)
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– (514.18pt, -567.75pt)
– (290.69pt, -567.75pt) – cycle

(57.024,-582.3)121T1cmrmn5. Khoo, F. S., Mark, M., Timmer, R. C., Ribeiro Martins, M. S., Foshee, E., Bugbee,
(75.024,-598.14)121T1cmrmnK., Renard, G., & Berea, A. (2023). Building Knowledge Graphs in Heliophysics and
(75.024,-614.1)121T1cmrmnAstrophysics. Natural Language Processing and Information Systems (NLDB 2023),
(75.024,-629.94)121T1cmrmnLecture Notes in Computer Science, vol. 13913, pp. 215-228.

[color_30046][even odd rule]
(75.024pt, -647.94pt) – (320.594pt, -647.94pt)
– (320.594pt, -647.94pt)
– (320.594pt, -647.1pt)
– (320.594pt, -647.1pt)
– (75.024pt, -647.1pt) – cycle

(57.024,-661.62)121T1cmrmn6. Kurtz, M. J., Eichhorn, G., Accomazzi, A., Grant, C. S., Murray, S. S., & Watson, J.
(75.024,-677.58)121T1cmrmnM. (2000). The NASA Astrophysics Data System: Overview. Astronomy and

(75.024,-73.29999)121T1cmrmnAstrophysics Supplement Series, 143(1), 41-59.

[color_30046][even odd rule]
(75.024pt, -91.29999pt) – (267.284pt, -91.29999pt)
– (267.284pt, -91.29999pt)
– (267.284pt, -90.45996pt)
– (267.284pt, -90.45996pt)
– (75.024pt, -90.45996pt) – cycle

(57.024,-104.98)121T1cmrmn7. Meyer, L.-P., Stadler, C., Frey, J., Radtke, N., Junghanns, K., Meissner, R., Dziwis,
(75.024,-120.94)121T1cmrmnG., Bulert, K., & Martin, M. (2023). LLM-assisted Knowledge Graph Engineering:
(75.024,-136.78)121T1cmrmnExperiments with ChatGPT. arXiv preprint arXiv:2307.06917.

[color_30046][even odd rule]
(75.024pt, -154.78pt) – (298.604pt, -154.78pt)
– (298.604pt, -154.78pt)
– (298.604pt, -153.94pt)
– (298.604pt, -153.94pt)
– (75.024pt, -153.94pt) – cycle

(57.024,-168.46)121T1cmrmn8. Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2023). Unifying Large
(75.024,-184.42)121T1cmrmnLanguage Models and Knowledge Graphs: A Roadmap. arXiv preprint
(75.024,-200.26)121T1cmrmnarXiv:2301.12345. https://doi.org/10.48550/arXiv.2301.12345.

[color_30046][even odd rule]
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– (399.43pt, -201.58pt)
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(57.024,-216.1)121T1cmrmn9. Shu, D., Chen, T., Jin, M., Zhang, Y., Zhang, C., Du, M., & Zhang, Y. (2024).
(75.024,-231.94)121T1cmrmnKnowledge Graph Large Language Model (KG-LLM) for Link Prediction. arXiv
(75.024,-247.93)121T1cmrmnpreprint arXiv:2403.07311. https://doi.org/10.48550/arXiv.2403.07311.

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(57.024,-263.77)121T1cmrmn10. Sun, Z., Ting, Y.-S., Liang, Y., Duan, N., Huang, S., & Cai, Z. (2024). Knowledge
(75.024,-279.61)121T1cmrmnGraph in Astronomical Research with Large Language Models: Quantifying Driving
(75.024,-295.45)121T1cmrmnForces in Interdisciplinary Scientific Discovery. arXiv preprint arXiv:2406.01391.

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– (75.024pt, -312.73pt) – cycle

(57.024,-327.25)121T1cmrmn11. Traag, V. A., Waltman, L., & Van Eck, N. J. (2019). From Louvain to Leiden:
(75.024,-343.09)121T1cmrmnguaranteeing well-connected communities. Scientific Reports, 9(1).

[color_30046][even odd rule]
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(57.024,-374.89)121T1cmrmn12. Trajanoska, M., Stojanov, R., & Trajanov, D. (2023). Enhancing Knowledge Graph
(75.024,-390.73)121T1cmrmnConstruction Using Large Language Models. arXiv preprint arXiv:2305.04676.

[color_30046][even odd rule]
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(57.024,-422.43)121T1cmrmn13. Yang, L., Chen, H., Li, Z., Ding, X., & Wu X (2023). Give Us the Facts: Enhancing
(75.024,-438.27)121T1cmrmnLarge Language Models with Knowledge Graphs for Fact-aware Language
(75.024,-454.23)121T1cmrmnModeling. arXiv preprint arXiv:2306.11489.

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(57.024,-101.5)121T1cmrmnThe following prompt was used to fine-tune the GPT-4o language model for entity and
(57.024,-117.34)121T1cmrmnrelationship extraction in the context of Apollo craters and Apollo missions. The prompt
(57.024,-133.18)121T1cmrmnconsists of two examples, each containing a short passage of text and a structured
(57.024,-149.14)121T1cmrmnrepresentation of the extracted entities and their relationships. These examples were
(57.024,-164.98)121T1cmrmncarefully selected to showcase the desired input-output format and the types of entities
(57.024,-180.82)121T1cmrmnand relationships relevant to the astronomical domain.
(57.024,-217.42)91T1cmrmnBased on the following two examples, extract entities and relations from the provided
(57.024,-237.85)91T1cmrmnUse the following entity types:
(57.024,-248.05)91T1cmrmn# ENTITY TYPES:
(57.024,-268.45)91T1cmrmn  "instrument": "https://www.wikidata.org/wiki/Q751997", # astronomical instrument
(57.024,-278.65)91T1cmrmn  "telescope": "https://www.wikidata.org/wiki/Q148578", # space telescope
(57.024,-288.85)91T1cmrmn  "mission": "https://www.wikidata.org/wiki/Q2133344", # space mission
(57.024,-299.05)91T1cmrmn  "celestialObject": "https://www.wikidata.org/wiki/Q6999", # astronomical object
(57.024,-309.25)91T1cmrmn  "celestialRegion": "https://www.wikidata.org/wiki/Q203218", # spherical coordinate
(57.024,-329.65)91T1cmrmn  "celestialObjectRegion": "https://www.wikidata.org/wiki/Q12134", # celestial sphere
(57.024,-339.85)91T1cmrmn  "planetaryNomenclature": "https://www.wikidata.org/wiki/Q1463003", # planetary
(57.024,-350.05)91T1cmrmnnomenclature: system of uniquely identifying features on the surface of a planet or
(57.024,-360.25)91T1cmrmnnatural satellite
(57.024,-370.45)91T1cmrmn  "spacecraft": "https://www.wikidata.org/wiki/Q40218", # spacecraft
(57.024,-380.65)91T1cmrmn  "measure": "https://www.wikidata.org/wiki/Q39875001", # measure: standard against which
(57.024,-390.85)91T1cmrmnsomething can be judged
(57.024,-401.05)91T1cmrmn  "period": "https://www.wikidata.org/wiki/Q392928", # period: subdivision of geological
(57.024,-411.27)91T1cmrmntime; shorter than an era and longer than an epoch
(57.024,-421.47)91T1cmrmn  "coordinate": "https://www.wikidata.org/wiki/Q3250736", # coordinate: number which
(57.024,-431.67)91T1cmrmncharacterizes position
(57.024,-441.75)91T1cmrmn  "research": "https://www.wikidata.org/wiki/Q42240", # research: systematic study
(57.024,-451.95)91T1cmrmnundertaken to increase knowledge
(57.024,-462.15)91T1cmrmn  "data": "https://www.wikidata.org/wiki/Q42848", # data: information arranged for
(57.024,-472.35)91T1cmrmnautomatic processing
(57.024,-502.95)91T1cmrmnUse the following relation types:
(57.024,-523.35)91T1cmrmn    "discovery": "https://www.wikidata.org/wiki/Q12772819", # discovery
(57.024,-533.55)91T1cmrmn    "studies": "http://www.wikidata.org/prop/direct/P2579", # studied in
(57.024,-543.75)91T1cmrmn    "measuredAs": "http://www.wikidata.org/prop/direct/P111", # measured physical
(57.024,-564.15)91T1cmrmn    "hasChild": "http://www.wikidata.org/prop/direct/P40", # child
(57.024,-574.38)91T1cmrmn    "describedBy": "http://www.wikidata.org/prop/direct/P1343", # described by source
(57.024,-584.58)91T1cmrmn    "locatedIn": "http://www.wikidata.org/prop/direct/P276", # location
(57.024,-594.78)91T1cmrmn    "hasUse": "http://www.wikidata.org/prop/direct/P366", # use
(57.024,-604.98)91T1cmrmn    "orbitalInclination": "http://www.wikidata.org/prop/direct/P2045", # orbital
(57.024,-625.38)91T1cmrmn    "hasCoordinates": "http://www.wikidata.org/prop/direct/P625", # coordinate location
(57.024,-635.58)91T1cmrmn    "statedIn": "https://www.wikidata.org/wiki/Property:P248", # to be used in the
(57.024,-645.78)91T1cmrmnreferences field to refer to the information document or database in which a claim is
(57.024,-666.18)91T1cmrmn    "creator": "https://www.wikidata.org/wiki/Property:P170", # creator
(57.024,-676.38)91T1cmrmn    "hasCause": "https://www.wikidata.org/wiki/Property:P828", # corrolates

(57.024,-79.65997)91T1cmrmn–> Beginning of example 1
(57.024,-100.06)91T1cmrmn# Specification From both the Apollo and Surveyor experience, it is known that small
(57.024,-110.26)91T1cmrmncraters, rather than blocks, may actually present a greater danger to landing vehicles
(57.024,-120.46)91T1cmrmnbecause they are far more abundant. In fact, except for very young Copernican units,
(57.024,-130.66)91T1cmrmnthe lunar surface is saturated with craters smaller than 100 m in diameter (Gault 1970).
(57.024,-140.86)91T1cmrmnThus, a lunar landing site will contain many craters, presenting a sub-horizontal surface
(57.024,-151.06)91T1cmrmnat the scale of any lander. Blocks, on the other hand, are much less common, especially
(57.024,-161.26)91T1cmrmnin areas away from fresh craters (Cameron and Coyle 1971; Cintala et al. 1982; Cintala
(57.024,-171.46)91T1cmrmnand McBride 1995; Lee et al. 1986; Moore 1971). The presence of small, shallow craters
(57.024,-181.66)91T1cmrmncan be assessed from images with large (> 70â—¦ ) incidence angles and meterscale
(57.024,-232.54)91T1cmrmn# Output
(57.024,-252.97)91T1cmrmn  {{
(57.024,-263.17)91T1cmrmn    "head": "Apollo",
(57.024,-273.37)91T1cmrmn    "head_type": "spacecraft",
(57.024,-283.57)91T1cmrmn    "relation": "discovery",
(57.024,-293.77)91T1cmrmn    "tail": "small craters present a greater danger to landing vehicles",
(57.024,-303.97)91T1cmrmn    "tail_type": "celestialObjectRegion"
(57.024,-314.17)91T1cmrmn  }},
(57.024,-324.37)91T1cmrmn  {{
(57.024,-334.57)91T1cmrmn    "head": "Surveyor",
(57.024,-344.77)91T1cmrmn    "head_type": "spacecraft",
(57.024,-354.97)91T1cmrmn    "relation": "discovery",
(57.024,-365.17)91T1cmrmn    "tail": "small craters present a greater danger to landing vehicles",
(57.024,-375.37)91T1cmrmn    "tail_type": "celestialObjectRegion"
(57.024,-385.57)91T1cmrmn  }},
(57.024,-395.77)91T1cmrmn  {{
(57.024,-405.99)91T1cmrmn    "head": "Apollo",
(57.024,-416.19)91T1cmrmn    "head_type": "spacecraft",
(57.024,-426.39)91T1cmrmn    "relation": "studies",
(57.024,-436.59)91T1cmrmn    "tail": "Moon",
(57.024,-446.79)91T1cmrmn    "tail_type": "celestialObject"
(57.024,-456.87)91T1cmrmn  }},
(57.024,-467.07)91T1cmrmn  {{
(57.024,-477.27)91T1cmrmn    "head": "Surveyor",
(57.024,-487.47)91T1cmrmn    "head_type": "spacecraft",
(57.024,-497.67)91T1cmrmn    "relation": "studies",
(57.024,-507.87)91T1cmrmn    "tail": "Moon",
(57.024,-518.07)91T1cmrmn    "tail_type": "celestialObject"
(57.024,-528.27)91T1cmrmn  }},
(57.024,-538.47)91T1cmrmn  {{
(57.024,-548.67)91T1cmrmn    "head": "Copernican units",
(57.024,-558.87)91T1cmrmn    "head_type": "period",
(57.024,-569.07)91T1cmrmn    "relation": "measuredAs",
(57.024,-579.3)91T1cmrmn    "tail": "lunar surface",
(57.024,-589.5)91T1cmrmn    "tail_type": "celestialObjectRegion"
(57.024,-599.7)91T1cmrmn  }},
(57.024,-609.9)91T1cmrmn  {{
(57.024,-620.1)91T1cmrmn    "head": "Moon",
(57.024,-630.3)91T1cmrmn    "head_type": "celestialObject",
(57.024,-640.5)91T1cmrmn    "relation": "hasChild",
(57.024,-650.7)91T1cmrmn    "tail": "lunar poles",
(57.024,-660.9)91T1cmrmn    "tail_type": "celestialObjectRegion"
(57.024,-671.1)91T1cmrmn  }},
(57.024,-681.3)91T1cmrmn  {{
(57.024,-691.496)91T1cmrmn    "head": "Moon",
(57.024,-701.696)91T1cmrmn    "head_type": "celestialObject",

(57.024,-69.46002)91T1cmrmn    "relation": "hasChild",
(57.024,-79.65997)91T1cmrmn    "tail": "lunar surface",
(57.024,-89.85999)91T1cmrmn    "tail_type": "celestialObjectRegion"
(57.024,-100.06)91T1cmrmn  }},
(57.024,-110.26)91T1cmrmn  {{
(57.024,-120.46)91T1cmrmn    "head": "lunar surface",
(57.024,-130.66)91T1cmrmn    "head_type": "celestialObjectRegion",
(57.024,-140.86)91T1cmrmn    "relation": "describedBy",
(57.024,-151.06)91T1cmrmn    "tail": "craters smaller than 100 m in diameter",
(57.024,-161.26)91T1cmrmn    "tail_type": "celestialObject"
(57.024,-171.46)91T1cmrmn  }},
(57.024,-181.66)91T1cmrmn  {{
(57.024,-191.74)91T1cmrmn    "head": "lunar landing site",
(57.024,-201.94)91T1cmrmn    "head_type": "celestialObjectRegion",
(57.024,-212.14)91T1cmrmn    "relation": "describedBy",
(57.024,-222.34)91T1cmrmn    "tail": "many craters, presenting a sub-horizontal surface at the scale of any
(57.024,-242.77)91T1cmrmn    "tail_type": "celestialRegion"
(57.024,-252.97)91T1cmrmn  }},
(57.024,-263.17)91T1cmrmn  {{
(57.024,-273.37)91T1cmrmn    "head": "blocks",
(57.024,-283.57)91T1cmrmn    "head_type": "celestialRegion",
(57.024,-293.77)91T1cmrmn    "relation": "locatedIn",
(57.024,-303.97)91T1cmrmn    "tail": "areas away from fresh craters",
(57.024,-314.17)91T1cmrmn    "tail_type": "celestialRegion"
(57.024,-324.37)91T1cmrmn  }},
(57.024,-354.97)91T1cmrmn–> End of example 1
(57.024,-365.17)91T1cmrmn–> Beginning of example 2
(57.024,-385.57)91T1cmrmn# Specification
(57.024,-395.77)91T1cmrmnUsing this Apollo-based “calibration” of crater degradation, Basilevsky et al. (2013)
(57.024,-405.99)91T1cmrmnemployed high-resolution images from the Lunar Reconnaissance Orbiter Camera (LROC) to
(57.024,-416.19)91T1cmrmnanalyze the frequency of meter-scale boulders in the ejecta deposits of these dated
(57.024,-426.39)91T1cmrmnApollo craters, as well as from a few other craters that were of intermediate or more
(57.024,-436.59)91T1cmrmnadvanced degradational state and relative age based on Basilevsky (1976). The number
(57.024,-446.79)91T1cmrmndensity (number per unit surface area) of boulders >2 m observed in the ejecta deposits
(57.024,-456.87)91T1cmrmnof these craters correlates well with absolute crater-formation age(s) as illustrated in
(57.024,-467.07)91T1cmrmnFig. 9.
(57.024,-507.87)91T1cmrmn# Output
(57.024,-528.27)91T1cmrmn  {{
(57.024,-538.47)91T1cmrmn    "head": "Basilevsky et al. (2013)",
(57.024,-548.67)91T1cmrmn    "head_type": "research",
(57.024,-558.87)91T1cmrmn    "relation": "statedIn",
(57.024,-569.07)91T1cmrmn    "tail": "high-resolution images from LROC",
(57.024,-579.3)91T1cmrmn    "tail_type": "data"
(57.024,-589.5)91T1cmrmn  }},
(57.024,-599.7)91T1cmrmn  {{
(57.024,-609.9)91T1cmrmn    "head": "high-resolution images",
(57.024,-620.1)91T1cmrmn    "head_type": "data",
(57.024,-630.3)91T1cmrmn    "relation": "creator",
(57.024,-640.5)91T1cmrmn    "tail": "Lunar Reconnaissance Orbiter Camera (LROC)",
(57.024,-650.7)91T1cmrmn    "tail_type": "instrument"
(57.024,-660.9)91T1cmrmn  }},
(57.024,-671.1)91T1cmrmn  {{
(57.024,-681.3)91T1cmrmn    "head": "Apollo craters",
(57.024,-691.496)91T1cmrmn    "head_type": "planetaryNomenclature",
(57.024,-701.696)91T1cmrmn    "relation": "hasChild",

(57.024,-69.46002)91T1cmrmn    "tail": "ejecta deposits",
(57.024,-79.65997)91T1cmrmn    "tail_type": "celestialRegion"
(57.024,-89.85999)91T1cmrmn  }},
(57.024,-100.06)91T1cmrmn  {{
(57.024,-110.26)91T1cmrmn    "head": "number density of boulders >2 m",
(57.024,-120.46)91T1cmrmn    "head_type": "measure",
(57.024,-130.66)91T1cmrmn    "relation": "locatedIn",
(57.024,-140.86)91T1cmrmn    "tail": "ejecta deposits of craters",
(57.024,-151.06)91T1cmrmn    "tail_type": "celestialRegion"
(57.024,-161.26)91T1cmrmn  }},
(57.024,-171.46)91T1cmrmn  {{
(57.024,-181.66)91T1cmrmn    "head": "number density of boulders >2 m",
(57.024,-191.74)91T1cmrmn    "head_type": "measure",
(57.024,-201.94)91T1cmrmn    "relation": "hasCause",
(57.024,-212.14)91T1cmrmn    "tail": "absolute crater-formation age(s)",
(57.024,-222.34)91T1cmrmn    "tail_type": "measure"
(57.024,-232.54)91T1cmrmn  }},
(57.024,-242.77)91T1cmrmn  {{
(57.024,-252.97)91T1cmrmn    "head": "meter-scale boulders",
(57.024,-263.17)91T1cmrmn    "head_type": "celestialObject",
(57.024,-273.37)91T1cmrmn    "relation": "locatedIn",
(57.024,-283.57)91T1cmrmn    "tail": "ejecta deposits",
(57.024,-293.77)91T1cmrmn    "tail_type": "celestialRegion"
(57.024,-303.97)91T1cmrmn  }},
(57.024,-314.17)91T1cmrmn  {{
(57.024,-324.37)91T1cmrmn    "head": "ejecta deposits",
(57.024,-334.57)91T1cmrmn    "head_type": "celestialRegion",
(57.024,-344.77)91T1cmrmn    "relation": "hasChild",
(57.024,-354.97)91T1cmrmn    "tail": "Apollo craters",
(57.024,-365.17)91T1cmrmn    "tail_type": "planetaryNomenclature"
(57.024,-375.37)91T1cmrmn  }},
(57.024,-385.57)91T1cmrmn  {{
(57.024,-395.77)91T1cmrmn    "head": "ejecta deposits",
(57.024,-405.99)91T1cmrmn    "head_type": "celestialRegion",
(57.024,-416.19)91T1cmrmn    "relation": "hasChild",
(57.024,-426.39)91T1cmrmn    "tail": "craters of intermediate or more advanced degradational state and relative
(57.024,-446.79)91T1cmrmn    "tail_type": "celestialObject"
(57.024,-456.87)91T1cmrmn  }},
(57.024,-477.27)91T1cmrmn–> End of example 2
(57.024,-497.67)91T1cmrmnFor the following specification, generate and extract entities and relations as in the
(57.024,-507.87)91T1cmrmnprovided example.
(57.024,-528.27)91T1cmrmn# Specification
(57.024,-569.07)91T1cmrmn# Output
(57.024,-607.74)121T1cmrmnThe prompt was designed to provide the GPT-4o model with representative examples
(57.024,-623.58)121T1cmrmnthat demonstrate the specific terminology, writing styles, and context of astronomical
(57.024,-639.42)121T1cmrmnliterature. By training the model on these examples, it learned to recognize the patterns
(57.024,-655.38)121T1cmrmnand structures necessary for accurate entity and relationship extraction in the given