Triple

T7540439
Position Surface form Disambiguated ID Type / Status
Subject Cauca River Valley E178260 entity
Predicate containsCity P294 FINISHED
Object Tuluá
Tuluá is a mid-sized Colombian city in the Valle del Cauca department, known as an agricultural and commercial hub in the fertile Cauca River Valley.
 E672079 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: Tuluá | Statement: [Cauca River Valley, containsCity, Tuluá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tuluá
Context triple: [Cauca River Valley, containsCity, Tuluá]
  • A. Tasqueña
    Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
  • B. Tupiza
    Tupiza is a small historic town in southern Bolivia known for its dramatic red-rock canyons and as a gateway to Andean landscapes and mining regions.
  • C. Tumeremo
    Tumeremo is a mining town in southeastern Venezuela known for its gold deposits and location within Bolívar State.
  • D. Luyanó
    Luyanó is a traditional working-class neighborhood in Havana, Cuba, known for its dense urban fabric and vibrant local culture.
  • E. Tagüeña
    Tagüeña is a Spanish surname most notably associated with Manuel Tagüeña, a Republican military officer and physicist active during the Spanish Civil War.
  • 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: Tuluá
Triple: [Cauca River Valley, containsCity, Tuluá]
Generated description
Tuluá is a mid-sized Colombian city in the Valle del Cauca department, known as an agricultural and commercial hub in the fertile Cauca River Valley.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tuluá
Target entity description: Tuluá is a mid-sized Colombian city in the Valle del Cauca department, known as an agricultural and commercial hub in the fertile Cauca River Valley.
  • A. Tasqueña
    Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
  • B. Tupiza
    Tupiza is a small historic town in southern Bolivia known for its dramatic red-rock canyons and as a gateway to Andean landscapes and mining regions.
  • C. Tumeremo
    Tumeremo is a mining town in southeastern Venezuela known for its gold deposits and location within Bolívar State.
  • D. Luyanó
    Luyanó is a traditional working-class neighborhood in Havana, Cuba, known for its dense urban fabric and vibrant local culture.
  • E. Tagüeña
    Tagüeña is a Spanish surname most notably associated with Manuel Tagüeña, a Republican military officer and physicist active during the Spanish Civil War.
  • 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_69c69f2be3888190a6667a27f8f195e9 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f873b17081908bb70aea0010d072 completed March 27, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84f18e4dc81909ecd73b2b06b8d9c completed March 28, 2026, 9:58 p.m.
NEDg Description generation batch_69c853bc094c8190ba4e7ecb069c2c02 completed March 28, 2026, 10:18 p.m.
NED2 Entity disambiguation (via description) batch_69c85412e6308190893a500e2395bd94 completed March 28, 2026, 10:20 p.m.
Created at: March 27, 2026, 3:48 p.m.