Triple

T1099405
Position Surface form Disambiguated ID Type / Status
Subject Warren Beatty E24343 entity
Predicate produced P490 FINISHED
Object Shampoo E125977 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: Shampoo | Statement: [Warren Beatty, produced, Shampoo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shampoo
Context triple: [Warren Beatty, produced, Shampoo]
  • A. Shampoo chosen
    "Shampoo" is a 1975 satirical romantic comedy film set on the eve of the 1968 U.S. presidential election, starring Warren Beatty as a Beverly Hills hairdresser entangled in complex romantic and social relationships.
  • B. Soap
    Soap is a satirical American television sitcom that parodied daytime soap operas and became a cult classic for its controversial, boundary-pushing humor.
  • C. Schwarzkopf
    Schwarzkopf is a German surname most prominently associated with U.S. Army General Norman Schwarzkopf Jr., who led coalition forces in the Gulf War.
  • D. Tinte
    Tinte is a small village in the Dutch province of South Holland, known for its rural character and annual local festivities.
  • E. Softsoap
    Softsoap is a popular personal care product line best known for its liquid hand soaps and body washes.
  • 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_69a4940542308190ac2a0b1f730b7cfc completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4b9be92688190838ce35cd67e01f3 completed March 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac538ee1ec8190b704bb8414fa0cef completed March 7, 2026, 4:34 p.m.
Created at: March 1, 2026, 7:43 p.m.