Triple

T2546367
Position Surface form Disambiguated ID Type / Status
Subject BFR E57910 entity
Predicate intendedDestination P2066 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: [BFR, intendedDestination, Mars]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mars
Context triple: [BFR, intendedDestination, 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_69ab4a5212d88190b989ce129f2ad87f completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd2e5152c8190b31a5e732d0dde44 completed March 7, 2026, 7:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69af5d0a884c81909d7f537a79ccb435 completed March 9, 2026, 11:51 p.m.
Created at: March 6, 2026, 9:47 p.m.