Triple

T21460803
Position Surface form Disambiguated ID Type / Status
Subject La Unión y el Fénix Español E529464 entity
Predicate nameElement P27866 FINISHED
Object “La Unión”
“La Unión” is the shortened name of the historic Spanish insurance company La Unión y el Fénix Español, once one of Spain’s most prominent insurers.
 E1487057 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: “La Unión” | Statement: [La Unión y el Fénix Español, nameElement, “La Unión”]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: “La Unión”
Context triple: [La Unión y el Fénix Español, nameElement, “La Unión”]
  • A. Unión
    Unión is an Argentine sports club best known for its professional football team based in Santa Fe.
  • B. La Unión
    La Unión is a Chilean city in the Los Ríos Region known for its agricultural activities, dairy production, and role as a local commercial center.
  • C. La Unión
    La Unión is a coastal city in southeastern El Salvador that serves as a major Pacific port and gateway on the Gulf of Fonseca.
  • D. La Unión
    La Unión is a town in northern Peru that serves as the administrative and commercial center of Dos de Mayo Province in the Huánuco Region.
  • E. La Unión
    La Unión is a municipality in Costa Rica known for its urban development and location within the Greater Metropolitan Area near the capital, San José.
  • 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: “La Unión”
Triple: [La Unión y el Fénix Español, nameElement, “La Unión”]
Generated description
“La Unión” is the shortened name of the historic Spanish insurance company La Unión y el Fénix Español, once one of Spain’s most prominent insurers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: “La Unión”
Target entity description: “La Unión” is the shortened name of the historic Spanish insurance company La Unión y el Fénix Español, once one of Spain’s most prominent insurers.
  • A. Unión
    Unión is an Argentine sports club best known for its professional football team based in Santa Fe.
  • B. La Unión
    La Unión is a Chilean city in the Los Ríos Region known for its agricultural activities, dairy production, and role as a local commercial center.
  • C. La Unión
    La Unión is a coastal city in southeastern El Salvador that serves as a major Pacific port and gateway on the Gulf of Fonseca.
  • D. La Unión
    La Unión is a municipality in Costa Rica known for its urban development and location within the Greater Metropolitan Area near the capital, San José.
  • E. La Unión
    La Unión is a town in northern Peru that serves as the administrative and commercial center of Dos de Mayo Province in the Huánuco Region.
  • 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_69e0c458133481908ae8b41a12c4edec completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9e9ee10ec8190a606aa64001af35e completed April 23, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09cfee56d881909e43099ba5586b3f completed May 17, 2026, 2:25 p.m.
NEDg Description generation batch_6a09de517bd48190b29a5712f951a62a completed May 17, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_6a09deee2a148190838b82083784dcd3 completed May 17, 2026, 3:29 p.m.
Created at: April 16, 2026, 6:09 p.m.