Triple

T27132339
Position Surface form Disambiguated ID Type / Status
Subject FaSinPat E681591 entity
Predicate relatedOrganization P37 FINISHED
Object Zanón workers' cooperative
The Zanón workers' cooperative is a worker-run ceramic factory in Neuquén, Argentina, internationally known as a flagship example of worker self-management after being occupied and restarted by its employees.
 E1758075 NE FINISHED

How this triple was built (2 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: Zanón workers' cooperative | Statement: [FaSinPat, relatedOrganization, Zanón workers' cooperative]
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: Zanón workers' cooperative
Triple: [FaSinPat, relatedOrganization, Zanón workers' cooperative]
Generated description
The Zanón workers' cooperative is a worker-run ceramic factory in Neuquén, Argentina, internationally known as a flagship example of worker self-management after being occupied and restarted by its employees.

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_69eefacbcc2081909ebf00daa23f1981 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6247832a48190b820e8c0c22ff5db completed May 2, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12481df7e48190a6c3fa02223377d3 completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a1249b3e9888190b3bae29310007be4 completed May 24, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a124a8690ec8190853768e7cebe4b4e completed May 24, 2026, 12:47 a.m.
Created at: April 27, 2026, 9:05 a.m.