Triple

T17015278
Position Surface form Disambiguated ID Type / Status
Subject Foul Play E412804 entity
Predicate cinematographyBy P1953 FINISHED
Object David M. Walsh E308850 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: David M. Walsh | Statement: [Foul Play, cinematographyBy, David M. Walsh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David M. Walsh
Context triple: [Foul Play, cinematographyBy, David M. Walsh]
  • A. David M. Walsh chosen
    David M. Walsh is an American cinematographer known for his work on numerous films, particularly comedies, during the 1970s and 1980s.
  • B. David I. Walsh
    David I. Walsh was an American Democratic politician from Massachusetts who served as both governor and U.S. senator and was influential in early 20th-century labor and public contract legislation.
  • C. David Walsh
    David Walsh is an Australian professional gambler, art collector, and entrepreneur best known as the founder of Hobart’s provocative Museum of Old and New Art (MONA).
  • D. Timothy R. R. Walsh
    Timothy R. R. Walsh is an academic chemist best known as the doctoral advisor of Nobel Prize–winning biochemist Roger Y. Tsien.
  • E. Michael P. Walsh
    Michael P. Walsh is an American local politician who serves as the mayor of East Hartford, Connecticut.
  • 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_69e3d47f198c8190b0473f638101f606 completed April 18, 2026, 6:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0148222034819089474594ee351b05 completed May 11, 2026, 3:08 a.m.
Created at: April 10, 2026, 5:33 a.m.