Triple

T17026098
Position Surface form Disambiguated ID Type / Status
Subject Martial Law E413066 entity
Predicate creator P184 FINISHED
Object Robin Green E268685 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: Robin Green | Statement: [Martial Law, creator, Robin Green]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robin Green
Context triple: [Martial Law, creator, Robin Green]
  • A. Robin Green chosen
    Robin Green is an American television writer and producer best known for her work on acclaimed series such as *The Sopranos*.
  • B. Mart Green
    Mart Green is an American businessman and Christian philanthropist best known as the founder of Mardel Christian & Education and the son of Hobby Lobby founder David Green.
  • C. Daniel Green
    Daniel Green is a music producer known for his work on the track "Paradise."
  • D. Richard Green
    Richard Green was an American boxing referee best known for officiating major heavyweight bouts, including the 1980 title fight between Larry Holmes and Muhammad Ali.
  • E. Martin Green
    Martin Green is a renowned Australian engineer and solar energy researcher recognized as a leading pioneer in photovoltaic technology.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d5d46a5081908bc5681621dd8534 completed April 18, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a011b53306081908972a0a5db474b6c completed May 10, 2026, 11:57 p.m.
Created at: April 10, 2026, 5:33 a.m.