Triple

T19669261
Position Surface form Disambiguated ID Type / Status
Subject Human Nature E472288 entity
Predicate lyricist P1360 FINISHED
Object John Bettis NE NERFINISHED

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: John Bettis | Statement: [Human Nature, lyricist, John Bettis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Bettis
Context triple: [Human Nature, lyricist, John Bettis]
  • A. John Bettis chosen
    John Bettis is an American lyricist and songwriter known for penning numerous pop and television theme songs, including hits for artists like The Carpenters and Michael Jackson.
  • B. Ben Childress
    Ben Childress is a central character in the 1978 Brian De Palma horror-thriller "The Fury," known for his involvement with deadly psychic powers and government conspiracies.
  • C. Don Baylor
    Don Baylor was an American Major League Baseball slugger and later manager, renowned for his power hitting, durability, and leadership on and off the field.
  • D. Bill Romo
    Bill Romo is an individual notable for sharing the surname Romo, which is associated with several public figures in sports and entertainment.
  • E. Tom Burleson
    Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e514f2e08190ba70a4449519d218 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6416ad1b481908d2890d8c21aac5c completed April 20, 2026, 3:08 p.m.
Created at: April 10, 2026, 1:45 p.m.