Triple

T4983910
Position Surface form Disambiguated ID Type / Status
Subject Selene E111954 entity
Predicate domain P87 FINISHED
Object Moon E4619 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Moon | Statement: [Selene, domain, Moon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moon
Context triple: [Selene, domain, Moon]
  • A. Moon
    Moon is a 2009 science fiction film directed by Duncan Jones that follows a solitary astronaut nearing the end of his three-year stint on a lunar mining base, exploring themes of identity, memory, and corporate ethics.
  • B. Moon chosen
    The Moon is Earth's only natural satellite, a rocky celestial body that orbits our planet and significantly influences tides, calendars, and human culture.
  • C. Luna
    Luna is the natural satellite of Earth, renowned for its phases, influence on tides, and prominence in human culture and mythology.
  • D. Luna
    Luna was an ancient Roman town in northern Italy that served as a key urban and commercial center for the Ligurian region.
  • E. Mwenezi
    Mwenezi is a rural district and communal area in southern Zimbabwe known for cattle ranching, sugar estates, and its location along the Mwenezi River in Masvingo Province.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69bd441adc208190b70a033a0741d01e completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd7255d7b4819098b537df5b1a4c3c completed March 20, 2026, 4:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69be8a1891c48190b85bec5e97f75e44 completed March 21, 2026, 12:07 p.m.
Created at: March 20, 2026, 1:33 p.m.