Triple

T10558658
Position Surface form Disambiguated ID Type / Status
Subject Estadio Neza 86 E249154 entity
Predicate hasNickname P39 FINISHED
Object Neza 86
Neza 86 is a football stadium in Ciudad Nezahualcóyotl, Mexico, best known as the former home ground of Club Deportivo Neza and for hosting matches during the 1986 FIFA World Cup.
 E870557 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: Neza 86 | Statement: [Estadio Neza 86, hasNickname, Neza 86]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Neza 86
Context triple: [Estadio Neza 86, hasNickname, Neza 86]
  • A. Neka
    Neka is a city in northern Iran known for its location near the Caspian Sea and its role as an industrial and agricultural center in Mazandaran Province.
  • B. Neea
    Neea is a genus of flowering plants in the four o'clock family, comprising mostly tropical trees and shrubs native to the Americas.
  • C. Citura
    Citura is the public transport operator responsible for managing Reims’ urban transit network, including its tramway system, in northeastern France.
  • D. Zezuru
    Zezuru is a major dialect of the Shona language spoken primarily in central and northern Zimbabwe.
  • E. Neox
    Neox is a Spanish television channel owned by Atresmedia that primarily targets young audiences with a mix of series, films, and entertainment programs.
  • 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: Neza 86
Triple: [Estadio Neza 86, hasNickname, Neza 86]
Generated description
Neza 86 is a football stadium in Ciudad Nezahualcóyotl, Mexico, best known as the former home ground of Club Deportivo Neza and for hosting matches during the 1986 FIFA World Cup.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Neza 86
Target entity description: Neza 86 is a football stadium in Ciudad Nezahualcóyotl, Mexico, best known as the former home ground of Club Deportivo Neza and for hosting matches during the 1986 FIFA World Cup.
  • A. Neka
    Neka is a city in northern Iran known for its location near the Caspian Sea and its role as an industrial and agricultural center in Mazandaran Province.
  • B. Neea
    Neea is a genus of flowering plants in the four o'clock family, comprising mostly tropical trees and shrubs native to the Americas.
  • C. Citura
    Citura is the public transport operator responsible for managing Reims’ urban transit network, including its tramway system, in northeastern France.
  • D. Zezuru
    Zezuru is a major dialect of the Shona language spoken primarily in central and northern Zimbabwe.
  • E. Neox
    Neox is a Spanish television channel owned by Atresmedia that primarily targets young audiences with a mix of series, films, and entertainment programs.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5271e65688190bcf7931373d87f94 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d93486c7288190a2ccb822fc968919 completed April 10, 2026, 5:33 p.m.
NEDg Description generation batch_69d938c979788190b11b02748ed44153 completed April 10, 2026, 5:52 p.m.
NED2 Entity disambiguation (via description) batch_69d9398b63f08190910dd838ad11de6e completed April 10, 2026, 5:55 p.m.
Created at: April 6, 2026, 12:35 p.m.