Triple

T2433032
Position Surface form Disambiguated ID Type / Status
Subject Doom franchise E52890 entity
Predicate setting P1957 FINISHED
Object Mars E20154 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: Mars | Statement: [Doom franchise, setting, Mars]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mars
Context triple: [Doom franchise, setting, Mars]
  • A. Mars chosen
    Mars is the fourth planet from the Sun, a cold, desert-like world often called the "Red Planet" due to its iron-rich surface and a prime target in the search for past or present extraterrestrial life.
  • B. Terra
    Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
  • C. Venus
    Venus is the second planet from the Sun, known for its dense, toxic atmosphere, extreme surface temperatures, and bright visibility in Earth's sky.
  • D. Venus
    Venus is the Roman goddess of love, beauty, and fertility, often depicted as the divine ancestor and protector of Aeneas and the Roman people.
  • E. Venus
    "Venus" is a 2006 British comedy-drama film directed by Roger Michell, starring Peter O'Toole as an aging actor whose life is shaken up by his unexpected relationship with a young woman.
  • 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_69ab4959bcc0819083246f9fb10439e3 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc9caaa208190994767f07aebd2b9 completed March 7, 2026, 6:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf6b71c481908a5ff20edb09de14 completed March 9, 2026, 12:39 p.m.
Created at: March 6, 2026, 9:43 p.m.