Triple

T723274
Position Surface form Disambiguated ID Type / Status
Subject Lima Region E14664 entity
Predicate administrativeCenter P1474 FINISHED
Object Huacho E88773 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: Huacho | Statement: [Lima Region, administrativeCenter, Huacho]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Huacho
Context triple: [Lima Region, administrativeCenter, Huacho]
  • A. Huacho chosen
    Huacho is a coastal city in central Peru that serves as an important commercial and agricultural hub north of Lima.
  • B. Zipaquirá
    Zipaquirá is a historic Colombian city famed for its underground Salt Cathedral and colonial architecture, located north of Bogotá.
  • C. Huancavelica
    Huancavelica is a historic Andean city in central Peru, renowned as one of the colonial era’s most important mercury mining centers.
  • D. Arequipa
    Arequipa is Peru’s second-largest city, known for its colonial architecture built from white volcanic stone and its dramatic setting beneath the Misti volcano.
  • E. Puno
    Puno is a city in southeastern Peru on the shores of Lake Titicaca, known as a cultural center of the Andean highlands and a gateway to the lake’s islands.
  • 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_69a4934c753c81909b309027e48b9b3a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5a5360c8190b16e1e4f4206d0aa completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a65e3c22f08190b71734d9605a92f6 completed March 3, 2026, 4:06 a.m.
Created at: March 1, 2026, 7:37 p.m.