Triple

T9501185
Position Surface form Disambiguated ID Type / Status
Subject Jeremy Corbyn E229142 entity
Predicate spouse P13 FINISHED
Object Laura Alvarez E229142 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: Laura Alvarez | Statement: [Jeremy Corbyn, spouse, Laura Alvarez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Alvarez
Context triple: [Jeremy Corbyn, spouse, Laura Alvarez]
  • A. Laura Alvarez chosen
    Laura Alvarez is a Mexican-born businesswoman best known as the wife of former UK Labour Party leader Jeremy Corbyn.
  • B. Martha Sandoval
    Martha Sandoval is the plaintiff whose challenge to Alabama's English-only driver's license policy led to the landmark U.S. Supreme Court case Alexander v. Sandoval on private enforcement of disparate-impact regulations.
  • C. Rosaura De la Garza
    Rosaura De la Garza is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as the dutiful yet conflicted daughter who embodies traditional expectations and rivalry within the De la Garza family.
  • D. Yolanda Magaña
    Yolanda Magaña is a person notable enough to be recognized as a significant bearer of the surname Magaña.
  • E. Maria Gonzalez
    Maria Gonzalez is known as the spouse of Academy Award–winning film editor Stephen Mirrione.
  • 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_69ca84753660819098e8d416e89e26ae completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd983d4b708190a4dfef1246986a26 completed April 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c02de448190a2feea16d5461726 completed April 4, 2026, 5:36 p.m.
Created at: March 30, 2026, 7:57 p.m.