Triple

T16081985
Position Surface form Disambiguated ID Type / Status
Subject De la Garza family E390131 entity
Predicate hasMember P10 FINISHED
Object Tita De la Garza E388925 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: Tita De la Garza | Statement: [De la Garza family, hasMember, Tita De la Garza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tita De la Garza
Context triple: [De la Garza family, hasMember, Tita De la Garza]
  • A. Tita De la Garza chosen
    Tita De la Garza is the passionate, magically gifted protagonist of Laura Esquivel’s novel "Like Water for Chocolate," whose emotions infuse the food she cooks.
  • B. Gertrudis De la Garza
    Gertrudis De la Garza is a passionate and rebellious character from Laura Esquivel’s novel "Like Water for Chocolate," known for defying her strict family and societal expectations.
  • C. Lola Flores
    Lola Flores was a celebrated Spanish flamenco singer, dancer, and actress, iconic in 20th-century Spanish popular culture.
  • D. Carmen Cortez
    Carmen Cortez is a resourceful young spy and one of the two sibling protagonists in the Spy Kids film series.
  • E. María Elena
    María Elena is a small Chilean mining town in the Antofagasta Region, historically known as one of the last nitrate (saltpeter) company towns in the world.
  • 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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1844b86288190ad0452aad6bdd5fb completed April 17, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a011b31b3b4819095d0c24ee471e2a9 completed May 10, 2026, 11:56 p.m.
Created at: April 10, 2026, 4:57 a.m.