Triple

T1011557
Position Surface form Disambiguated ID Type / Status
Subject The Trolley Song E21834 entity
Predicate writer P1360 FINISHED
Object Hugh Martin E127847 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: Hugh Martin | Statement: [The Trolley Song, writer, Hugh Martin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hugh Martin
Context triple: [The Trolley Song, writer, Hugh Martin]
  • A. Hugh Martin chosen
    Hugh Martin was an American composer and songwriter best known for his work on classic Hollywood musicals, including writing enduring standards for films like "Meet Me in St. Louis."
  • B. Joel McNeely
    Joel McNeely is an American composer and conductor best known for his work on film and television scores, including numerous projects for Disney and other major studios.
  • C. Gregory Martin
    Gregory Martin was a 16th-century English Catholic scholar and priest best known for producing the first complete English translation of the Bible from the Latin Vulgate, which became the Douay–Rheims Bible.
  • D. Jud Ashman
    Jud Ashman is an American local politician who serves as the mayor of Gaithersburg, Maryland.
  • E. Chuck Cecil
    Chuck Cecil is a former American football safety best known for his hard-hitting play in the NFL and his standout collegiate career at the University of Arizona.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7a743cc8190a46e6a14e3e8130f completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5995ad6c8190a324094151442bce completed March 7, 2026, 5 p.m.
Created at: March 1, 2026, 7:41 p.m.