Triple

T2573136
Position Surface form Disambiguated ID Type / Status
Subject Tabasco E57708 entity
Predicate hasMajorCity P316 FINISHED
Object Macuspana
Macuspana is a significant urban center and municipality in the Mexican state of Tabasco, known for its role in the region’s political and economic life.
 E281059 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: Macuspana | Statement: [Tabasco, hasMajorCity, Macuspana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Macuspana
Context triple: [Tabasco, hasMajorCity, Macuspana]
  • A. Guarijío
    Guarijío is an indigenous Uto-Aztecan language spoken by the Guarijío people of northern Mexico, particularly in the states of Chihuahua and Sonora.
  • B. Sibaté
    Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
  • C. Alausí
    Alausí is a historic town in Ecuador known for its dramatic Andean setting and the famous Nariz del Diablo (Devil’s Nose) railway.
  • D. Sipakapense
    Sipakapense is a Mayan language spoken by the Sipakapense people of the western highlands of Guatemala.
  • E. Tafoya
    Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
  • 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: Macuspana
Triple: [Tabasco, hasMajorCity, Macuspana]
Generated description
Macuspana is a significant urban center and municipality in the Mexican state of Tabasco, known for its role in the region’s political and economic life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Macuspana
Target entity description: Macuspana is a significant urban center and municipality in the Mexican state of Tabasco, known for its role in the region’s political and economic life.
  • A. Guarijío
    Guarijío is an indigenous Uto-Aztecan language spoken by the Guarijío people of northern Mexico, particularly in the states of Chihuahua and Sonora.
  • B. Sibaté
    Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
  • C. Alausí
    Alausí is a historic town in Ecuador known for its dramatic Andean setting and the famous Nariz del Diablo (Devil’s Nose) railway.
  • D. Sipakapense
    Sipakapense is a Mayan language spoken by the Sipakapense people of the western highlands of Guatemala.
  • E. Tafoya
    Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
  • 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_69ab4a51410081908501dcf8bad9adc4 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd3853c848190970e8a2da16d726d completed March 7, 2026, 7:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69af83aefb00819095a6ab26f9bb61d9 completed March 10, 2026, 2:36 a.m.
NEDg Description generation batch_69af8483e06481908990180259beaa5a completed March 10, 2026, 2:40 a.m.
NED2 Entity disambiguation (via description) batch_69af84e909308190a6a1a2e818f263c4 completed March 10, 2026, 2:41 a.m.
Created at: March 6, 2026, 9:48 p.m.