Triple

T1390692
Position Surface form Disambiguated ID Type / Status
Subject Abby Aldrich Rockefeller E29949 entity
Predicate givenName P17 FINISHED
Object Abigail E29086 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: Abigail | Statement: [Abby Aldrich Rockefeller, givenName, Abigail]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Abigail
Context triple: [Abby Aldrich Rockefeller, givenName, Abigail]
  • A. Abigail chosen
    Abigail is a feminine given name of Hebrew origin meaning "my father is joy," historically popular in English-speaking countries.
  • B. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • C. Hannah Foster
    Hannah Foster is a notable individual recognized for her association with the surname Foster, though specific widely known biographical details about her are limited.
  • D. Patricia
    Patricia is a feminine given name of Latin origin, commonly used in English-speaking countries.
  • E. Betsy
    Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
  • 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_69a498dc92f8819094a1108f8ac90f43 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c35e023c8190b45688796d90534b completed March 1, 2026, 10:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e657d3c8190962220e52e3e64c0 completed March 8, 2026, 5:51 a.m.
Created at: March 1, 2026, 7:59 p.m.