Triple

T11988175
Position Surface form Disambiguated ID Type / Status
Subject Isabel Scott Rorick E285335 entity
Predicate givenName P17 FINISHED
Object Isabel E114378 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: Isabel | Statement: [Isabel Scott Rorick, givenName, Isabel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabel
Context triple: [Isabel Scott Rorick, givenName, Isabel]
  • A. Isabel
    Isabel is a Spanish historical drama television series centered on the life and reign of Queen Isabella I of Castile.
  • B. Isabel chosen
    Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
  • C. Isabelle
    Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
  • D. Isabelle
    Isabelle is a prominent interactive theorem prover and proof assistant widely used in formal verification and mathematical logic research.
  • E. Isabelle
    Isabelle is a feminine given name of French origin, commonly used in many countries and cultures.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903ae28708190a826bad1624343eb completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f472492ebc8190b064e691bb70e356 completed May 1, 2026, 9:28 a.m.
Created at: April 8, 2026, 9:46 p.m.