Triple

T4655468
Position Surface form Disambiguated ID Type / Status
Subject Panguilemo Airport E102397 entity
Predicate cityServed P82 FINISHED
Object Talca E140968 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: Talca | Statement: [Panguilemo Airport, cityServed, Talca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Talca
Context triple: [Panguilemo Airport, cityServed, Talca]
  • A. Talca chosen
    Talca is a major city in central Chile known as an administrative, commercial, and agricultural hub in the Maule Valley.
  • B. Talcahuano
    Talcahuano is a major Chilean port city and naval base known for its shipyards and fishing industry.
  • C. Rancagua
    Rancagua is a major Chilean city known for its mining industry and historical significance in the country’s independence, serving as an important commercial and administrative center south of Santiago.
  • D. Maipú
    Maipú is a populous commune and suburb of Santiago, Chile, known for its residential areas, commercial activity, and historical significance in the Santiago Metropolitan Region.
  • E. Maipú
    Maipú is a renowned wine-producing region in Argentina’s Mendoza Province, noted for its high-quality Malbec and other varietals.
  • 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_69bd43d823288190952279faa0d1d066 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6317ba70819089145766d3462e57 completed March 20, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb0bc858c8190b70709f5ed743ee6 completed March 21, 2026, 2:52 p.m.
Created at: March 20, 2026, 1:14 p.m.