Triple

T1618152
Position Surface form Disambiguated ID Type / Status
Subject Copiapó E34766 entity
Predicate nearbySettlement P350 FINISHED
Object Tierra Amarilla E122973 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: Tierra Amarilla | Statement: [Copiapó, nearbySettlement, Tierra Amarilla]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tierra Amarilla
Context triple: [Copiapó, nearbySettlement, Tierra Amarilla]
  • A. Tierra Amarilla chosen
    Tierra Amarilla is a small mining-oriented town and commune in northern Chile’s Atacama Desert, known for its copper and gold production.
  • B. Durango
    Durango is a state in north-central Mexico known for its rugged mountainous terrain, significant mining history, and role as a setting for classic Western films.
  • C. Alamosa, Colorado
    Alamosa, Colorado is a small city in the San Luis Valley known as a regional hub for southern Colorado and a gateway to Great Sand Dunes National Park.
  • D. Chaco Province
    Chaco Province is an administrative region in northeastern Argentina known for its vast plains, subtropical climate, and significant indigenous and rural populations.
  • E. Acaponeta
    Acaponeta is a town and municipality in the Mexican state of Nayarit, known for its agricultural economy and location near the Acaponeta River.
  • 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_69a885ffc5ec819091afa325d5f9611c completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909addb348190a80a97422efcaa63 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfb93ea5481908f21378715bab13d completed March 8, 2026, 10:43 p.m.
Created at: March 4, 2026, 7:28 p.m.