Triple

T2918930
Position Surface form Disambiguated ID Type / Status
Subject Linares y Pombo E78672 entity
Predicate hasPart P35 FINISHED
Object Linares E307095 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: Linares | Statement: [Linares y Pombo, hasPart, Linares]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Linares
Context triple: [Linares y Pombo, hasPart, Linares]
  • A. Linares
    Linares is a provincial capital and agricultural city in Chile’s Maule Region, known for its surrounding farmlands and wine production.
  • B. Linares chosen
    Linares is a Spanish football club known for being one of the early teams in the playing career of manager Rafael Benítez.
  • C. Lucena
    Lucena is a coastal city in the Philippines that serves as the capital and commercial hub of Quezon Province in the Southern Tagalog region.
  • D. Linares y Pombo
    Linares y Pombo is the compound Spanish surname of Arsenio Linares y Pombo, a notable Spanish military officer and politician of the late 19th and early 20th centuries.
  • E. Durán
    Durán is an Ecuadorian city in the Guayas Province, located across the Guayas River from Guayaquil and serving as an important transport and industrial hub.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a53f8c8190b188d549f1161e84 completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0562fc5f081909c9130f71f379a24 completed March 10, 2026, 5:34 p.m.
Created at: March 8, 2026, 2:54 p.m.