Triple

T3099637
Position Surface form Disambiguated ID Type / Status
Subject Bailén E64682 entity
Predicate isLocatedNear P350 FINISHED
Object Linares
Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
E328051 NE FINISHED

How this triple was built (4 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: [Bailén, isLocatedNear, Linares]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Linares
Context triple: [Bailén, isLocatedNear, 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
    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. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Linares
Triple: [Bailén, isLocatedNear, Linares]
Generated description
Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Linares
Target entity description: Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
  • 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
    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 historic city in the province of Córdoba, Andalusia, southern Spain, known for its rich cultural heritage and former Jewish community.
  • D. 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.
  • E. 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.
  • F. None of above. chosen

Provenance (5 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_69ad857dc98481909e585dc3372e3ed5 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada269a9188190aada5b3799d4dfd7 completed March 8, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2037cc5fc819084a441ebb045142b completed March 12, 2026, 12:06 a.m.
NEDg Description generation batch_69b20412e6f8819097f30e50a4141cbe completed March 12, 2026, 12:08 a.m.
NED2 Entity disambiguation (via description) batch_69b207e671888190ab8d97ad661bb5bd completed March 12, 2026, 12:25 a.m.
Created at: March 8, 2026, 3:03 p.m.