Triple

T24992710
Position Surface form Disambiguated ID Type / Status
Subject Colón de Santa Fe E625487 entity
Predicate rival P437 FINISHED
Object Unión de Santa Fe
Unión de Santa Fe is a traditional Argentine football club from the city of Santa Fe, best known for its passionate fan base and intense local derby against Colón.
E1660213 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: Unión de Santa Fe | Statement: [Colón de Santa Fe, rival, Unión de Santa Fe]
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: Unión de Santa Fe
Triple: [Colón de Santa Fe, rival, Unión de Santa Fe]
Generated description
Unión de Santa Fe is a traditional Argentine football club from the city of Santa Fe, best known for its passionate fan base and intense local derby against Colón.

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_69e2ff2611c081908710457fbe6d376b completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f44a4639e8819090ce27c835eec2f0 completed May 1, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10336ac6e481908430de7847492512 completed May 22, 2026, 10:43 a.m.
NEDg Description generation batch_6a10372f702c8190a44f791c49f7b5f0 completed May 22, 2026, 10:59 a.m.
NED2 Entity disambiguation (via description) batch_6a1037e1863881909a08a9d79d50437a completed May 22, 2026, 11:02 a.m.
Created at: April 18, 2026, 6:04 a.m.