Triple

T13752904
Position Surface form Disambiguated ID Type / Status
Subject Julia Gardiner Tyler E330398 entity
Predicate familyName P18 FINISHED
Object Tyler E674057 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: Tyler | Statement: [Julia Gardiner Tyler, familyName, Tyler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyler
Context triple: [Julia Gardiner Tyler, familyName, Tyler]
  • A. Tyler
    Tyler is a character in the 2015 horror-thriller film "The Visit," serving as one of the two grandchildren whose unsettling stay with their grandparents drives the movie’s plot.
  • B. Tyler
    Tyler is a surname most prominently associated with American actress Liv Tyler and various other notable figures in entertainment and public life.
  • C. Tyler
    Tyler is a fictional character appearing in the American television series "Kristin."
  • D. Tyler chosen
    Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "house builder."
  • E. Tyler
    Tyler is a mid-sized city in East Texas known for its rose cultivation, annual Texas Rose Festival, and role as a regional medical and educational hub.
  • 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_69d81c573f288190aa2403d484fa3d49 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0215cfa08190aaed8b089aff217b completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b06d9fd48190a10b86a0d68fac70 completed May 3, 2026, 8:30 p.m.
Created at: April 9, 2026, 10:09 p.m.