Triple

T3238690
Position Surface form Disambiguated ID Type / Status
Subject Mars E67915 entity
Predicate consort P13 FINISHED
Object Venus E116817 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: Venus | Statement: [Mars, consort, Venus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Venus
Context triple: [Mars, consort, Venus]
  • A. 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.
  • B. Venus chosen
    Venus is the Roman goddess of love, beauty, and fertility, often depicted as the divine ancestor and protector of Aeneas and the Roman people.
  • C. 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.
  • D. Venus and Mars
    "Venus and Mars" is a 1975 rock album by Paul McCartney and Wings, known for its melodic pop-rock style and serving as the follow-up to the hugely successful "Band on the Run."
  • E. Merkur
    Merkur was a short-lived automotive marque created by Ford in the 1980s to sell European-designed performance and luxury cars in the North American market.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef3b04081908ce9b788e2e5c63c completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2774a97c481908be820bc5ddf786d completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.