Triple

T14879417
Position Surface form Disambiguated ID Type / Status
Subject Diane Freeling E349958 entity
Predicate hasChild P369 FINISHED
Object Carol Anne Freeling E571824 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: Carol Anne Freeling | Statement: [Diane Freeling, hasChild, Carol Anne Freeling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carol Anne Freeling
Context triple: [Diane Freeling, hasChild, Carol Anne Freeling]
  • A. Carol Anne Freeling chosen
    Carol Anne Freeling is the young girl at the center of the supernatural events in the Poltergeist film series, known for being abducted by malevolent spirits through her family's television.
  • B. Lisa Freeman
    Lisa Freeman is an American actress known for her supporting role in the 1981 romantic comedy film "Modern Romance."
  • C. Dawn Freeman
    Dawn Freeman is known as the wife of late American heavyweight boxer and former WBO champion Tommy Morrison.
  • D. Mary Frey
    Mary Frey is known as the wife of American automobile executive and engineer Donald Frey, a key figure behind the development of the Ford Mustang.
  • E. Laura Bickford
    Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5e622388190b2bf91cd10b9821d completed April 15, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69fef885b07c8190af5e33303af9fbea completed May 9, 2026, 9:04 a.m.
Created at: April 10, 2026, 1:55 a.m.