Triple

T8753406
Position Surface form Disambiguated ID Type / Status
Subject central New Mexico E208015 entity
Predicate contains P35 FINISHED
Object Belen
Belen is a small city in central New Mexico known as a regional transportation hub and bedroom community for the Albuquerque metropolitan area.
 E755437 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: Belen | Statement: [central New Mexico, contains, Belen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Belen
Context triple: [central New Mexico, contains, Belen]
  • A. Belén
    Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
  • B. Belmonte
    Belmonte is a historic town in Portugal known for its medieval castle and strong Jewish heritage, located in the country's Centro Region.
  • C. Rosario
    Rosario is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • D. Rosario
    Rosario is a first-class agricultural municipality in the province of Batangas in the Philippines, known for its coconut and rice farming.
  • E. Rosario
    Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
  • 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: Belen
Triple: [central New Mexico, contains, Belen]
Generated description
Belen is a small city in central New Mexico known as a regional transportation hub and bedroom community for the Albuquerque metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Belen
Target entity description: Belen is a small city in central New Mexico known as a regional transportation hub and bedroom community for the Albuquerque metropolitan area.
  • A. Belén
    Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
  • B. Belmonte
    Belmonte is a historic town in Portugal known for its medieval castle and strong Jewish heritage, located in the country's Centro Region.
  • C. Rosario
    Rosario is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • D. Rosario
    Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
  • E. Rosario
    Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
  • 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_69ca835cd6b08190bd7c63db92f53c86 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5dd714dc8190bccc4d52f988958d completed March 31, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf4326d8cc8190900f5f91da6ef6c8 completed April 3, 2026, 4:33 a.m.
NEDg Description generation batch_69cf4462da648190a621397fa88dd4bd completed April 3, 2026, 4:38 a.m.
NED2 Entity disambiguation (via description) batch_69cf454c4d248190a925b15c23af1a24 completed April 3, 2026, 4:42 a.m.
Created at: March 30, 2026, 6:39 p.m.