Triple

T2789724
Position Surface form Disambiguated ID Type / Status
Subject La Rioja Province E61898 entity
Predicate hasCity P316 FINISHED
Object Chepes
Chepes is a small city in western Argentina that serves as an important local center within La Rioja Province.
E298903 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: Chepes | Statement: [La Rioja Province, hasCity, Chepes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chepes
Context triple: [La Rioja Province, hasCity, Chepes]
  • A. Fez
    Fez is a historic imperial city in northern Morocco renowned for its well-preserved medieval medina, traditional craftsmanship, and status as a major cultural and religious center.
  • B. Pericos
    Pericos is the popular nickname for Spanish football club RCD Espanyol and its supporters.
  • C. Capcir
    Capcir is a high-altitude plateau and historical region in the eastern Pyrenees of southern France, known for its mountain landscapes, lakes, and Catalan cultural heritage.
  • D. Chiers
    The Chiers is a river in Western Europe that flows through Luxembourg, Belgium, and France before joining the Meuse.
  • E. Vinzelles
    Vinzelles is a small wine-producing village in France’s Mâconnais region, known for its white Burgundy wines made primarily from Chardonnay.
  • 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: Chepes
Triple: [La Rioja Province, hasCity, Chepes]
Generated description
Chepes is a small city in western Argentina that serves as an important local center within La Rioja Province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Chepes
Target entity description: Chepes is a small city in western Argentina that serves as an important local center within La Rioja Province.
  • A. Fez
    Fez is a historic imperial city in northern Morocco renowned for its well-preserved medieval medina, traditional craftsmanship, and status as a major cultural and religious center.
  • B. Pericos
    Pericos is the popular nickname for Spanish football club RCD Espanyol and its supporters.
  • C. Capcir
    Capcir is a high-altitude plateau and historical region in the eastern Pyrenees of southern France, known for its mountain landscapes, lakes, and Catalan cultural heritage.
  • D. Chiers
    The Chiers is a river in Western Europe that flows through Luxembourg, Belgium, and France before joining the Meuse.
  • E. Vinzelles
    Vinzelles is a small wine-producing village in France’s Mâconnais region, known for its white Burgundy wines made primarily from Chardonnay.
  • 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_69ab4b7f51d881908768300ebd2fbdae completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abddb4ef3081909122840a357801bf completed March 7, 2026, 8:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6589558819088442f09db328dac completed March 10, 2026, 7:20 a.m.
NEDg Description generation batch_69afc6d485d88190b281abec460ff24d completed March 10, 2026, 7:23 a.m.
NED2 Entity disambiguation (via description) batch_69afc750fee08190bf3c112f6f204af4 completed March 10, 2026, 7:25 a.m.
Created at: March 6, 2026, 9:58 p.m.