Triple

T1862219
Position Surface form Disambiguated ID Type / Status
Subject San Luis Potosí E34840 entity
Predicate bordersWith P224 FINISHED
Object Hidalgo E31143 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: Hidalgo | Statement: [San Luis Potosí, bordersWith, Hidalgo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hidalgo
Context triple: [San Luis Potosí, bordersWith, Hidalgo]
  • A. Hidalgo chosen
    Hidalgo is a central Mexican state known for its mountainous terrain, rich mining history, and diverse indigenous cultural heritage.
  • B. Navojoa
    Navojoa is a city in the southern part of the state of Sonora, Mexico, known as an agricultural and commercial center in the Mayo River valley.
  • C. San Felipe
    San Felipe is a historic city in central Chile known for its agricultural surroundings and role as a commercial and administrative center in the Aconcagua Valley.
  • D. San Felipe
    San Felipe is a coastal town in Baja California, Mexico, known as a gateway to nearby natural attractions and desert and mountain landscapes.
  • E. Madero
    Madero is a coastal city in the Mexican state of Tamaulipas, known for its oil industry and the popular Miramar Beach on the Gulf of Mexico.
  • 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_69a88600b2f88190bc09303e68ab517e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb09e714881909cef0f7e77b5b3b9 completed March 7, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1d026748190a507872de85c908d completed March 8, 2026, 7:45 p.m.
Created at: March 4, 2026, 7:34 p.m.