Triple

T19549811
Position Surface form Disambiguated ID Type / Status
Subject María de la Luz Cervantes E489165 entity
Predicate readerResponseRole P72617 FINISHED
Object elicits empathy and horror at injustice LITERAL 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: elicits empathy and horror at injustice | Statement: [María de la Luz Cervantes, readerResponseRole, elicits empathy and horror at injustice]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: readerResponseRole
Context triple: [María de la Luz Cervantes, readerResponseRole, elicits empathy and horror at injustice]
  • A. roleInText
    Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
  • B. representedRole
    Indicates that one entity serves as a stand-in, proxy, or representative performing a role on behalf of another entity.
  • C. audienceRole chosen
    Indicates the role or function an entity has as part of an audience in relation to another entity or event.
  • D. roleInVox
    Indicates that an entity holds a specific role or function within a Vox-related context, such as a project, system, or organization named or described as "Vox."
  • E. literaryRole
    Indicates the specific narrative or functional role an entity holds within a literary work or text.
  • F. None of above.

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_69d8e8dc5d8c8190a6d7bd8864f43ca0 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63d2f39188190976e8b6b111499a0 completed April 20, 2026, 2:50 p.m.
PD Predicate disambiguation batch_69e514d4df3c8190b7e9b3b4fdf9452a completed April 19, 2026, 5:45 p.m.
Created at: April 10, 2026, 1:41 p.m.