Triple

T3985636
Position Surface form Disambiguated ID Type / Status
Subject Sarah Jane Fulks E86863 entity
Predicate hasGivenName P17 FINISHED
Object Sarah E34678 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: Sarah | Statement: [Sarah Jane Fulks, hasGivenName, Sarah]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarah
Context triple: [Sarah Jane Fulks, hasGivenName, Sarah]
  • A. Sarah
    Sarah is the central protagonist of the story "Horse Girl," around whom the main narrative and character development revolve.
  • B. Sarah chosen
    Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
  • C. Sarah
    Sarah is the birth name of Margaret Fuller, the 19th-century American journalist, critic, and women's rights advocate associated with the Transcendentalist movement.
  • D. Jessica
    Jessica is a kind-hearted schoolteacher who becomes Mrs. Claus in the classic stop-motion Christmas special "Santa Claus Is Comin' to Town."
  • E. Jessica
    Jessica is a feminine given name of Hebrew origin, widely used in English-speaking countries and popularized by Shakespeare’s play "The Merchant of Venice."
  • 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_69aed93fd9d4819085d3b2137d2346cb completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef9dfaf28819081b547836d79b889 completed March 9, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5402bbe208190947321353c309c98 completed March 14, 2026, 11:02 a.m.
Created at: March 9, 2026, 3:33 p.m.