Triple

T3269769
Position Surface form Disambiguated ID Type / Status
Subject Rachel Cohen-Kagan E68615 entity
Predicate givenName P17 FINISHED
Object Rachel E69959 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: Rachel | Statement: [Rachel Cohen-Kagan, givenName, Rachel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rachel
Context triple: [Rachel Cohen-Kagan, givenName, Rachel]
  • A. Rachel
    Rachel is the famous bronze piggy bank sculpture and unofficial mascot of Seattle’s Pike Place Market, known for collecting donations for local social services.
  • B. Rachel chosen
    Rachel is a prominent biblical matriarch in the Book of Genesis, known as Jacob’s beloved wife and the mother of Joseph and Benjamin.
  • C. Sarah
    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.
  • D. 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.
  • E. Sarah
    Sarah is the central protagonist of the story "Horse Girl," around whom the main narrative and character development revolve.
  • 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_69ad859b54f881909bf530d549caf2fd completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adaff349148190beae8c0994b7ad83 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3341597448190805ff43effb9070c completed March 12, 2026, 9:45 p.m.
Created at: March 8, 2026, 3:09 p.m.