Triple

T5434810
Position Surface form Disambiguated ID Type / Status
Subject The Stripper E121980 entity
Predicate stars P1956 FINISHED
Object Louis Nye E322612 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: Louis Nye | Statement: [The Stripper, stars, Louis Nye]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Louis Nye
Context triple: [The Stripper, stars, Louis Nye]
  • A. Louis Nye chosen
    Louis Nye was an American comedian and character actor best known for his witty television appearances and recurring roles during the early years of late-night TV.
  • B. James W. Nye
    James W. Nye was a 19th-century American politician who served as the first governor of the Nevada Territory and later as a U.S. senator from Nevada.
  • C. Charles F. Roos
    Charles F. Roos was an American economist and mathematician known for his pioneering work in econometrics and contributions to the formalization of economic theory.
  • D. William A. Nitze
    William A. Nitze is an American energy and environmental policy expert known for his leadership roles in climate change initiatives and international environmental organizations.
  • E. William Bundy
    William Bundy was an American foreign policy expert and government official who played a key role in shaping U.S. strategy during the Vietnam War era.
  • 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_69bd46400768819092925d461c0b8432 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd91af68308190810c64e76c83fa46 completed March 20, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf6c5c053c8190a53df1b6b5088aab completed March 22, 2026, 4:13 a.m.
Created at: March 20, 2026, 2:06 p.m.