Triple

T6978883
Position Surface form Disambiguated ID Type / Status
Subject Anna Davenport Raines E161784 entity
Predicate givenName P17 FINISHED
Object Anna E161036 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: Anna | Statement: [Anna Davenport Raines, givenName, Anna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anna
Context triple: [Anna Davenport Raines, givenName, Anna]
  • A. Anna
    Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
  • B. Anna
    Anna is a character from the video game "Surfacing," likely serving as a key figure in the game's narrative or player interactions.
  • C. Anna chosen
    Anna is a feminine given name of Hebrew origin meaning "grace" or "favor," widely used across many cultures and languages.
  • D. Anna
    Anna is a character from the "Predator" franchise, appearing as one of the human figures caught up in the deadly encounters with the extraterrestrial hunter.
  • E. Anna
    Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
  • 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_69c68855dc0481909b4c7e9e9ed273db completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db6aa6188190af7656ff4d0e3230 completed March 27, 2026, 7:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c761b614e88190877455edd5f64cf1 completed March 28, 2026, 5:05 a.m.
Created at: March 27, 2026, 2:31 p.m.