Triple

T13710163
Position Surface form Disambiguated ID Type / Status
Subject Ana Navarro E328748 entity
Predicate twitterUsername P2943 FINISHED
Object ananavarro E18129 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: ananavarro | Statement: [Ana Navarro, twitterUsername, ananavarro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ananavarro
Context triple: [Ana Navarro, twitterUsername, ananavarro]
  • A. Vicálvaro
    Vicálvaro is a district in the southeast of Madrid, Spain, known for its residential character and educational institutions, including a campus of Rey Juan Carlos University.
  • B. Narvarte
    Narvarte is a centrally located neighborhood in Mexico City known for its residential character, mid-20th-century architecture, and growing array of restaurants, cafes, and nightlife.
  • C. Navarrenx
    Navarrenx is a historic fortified town in southwestern France, known for its well-preserved ramparts and strategic position in the Béarn region.
  • D. Nuñomoral
    Nuñomoral is a rural municipality in the province of Cáceres, Extremadura, Spain, known for its traditional architecture and location within the mountainous Las Hurdes region.
  • E. Navarro chosen
    Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
  • 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_69d80770b9bc81909f70c8c317d53cff completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dd43949e6c8190ae5e4fa119cde33a completed April 13, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69f79d52b3708190ae0945e65b271556 completed May 3, 2026, 7:09 p.m.
Created at: April 9, 2026, 9:54 p.m.