Triple

T23597977
Position Surface form Disambiguated ID Type / Status
Subject La Liga 1995-96 with Atlético de Madrid E582670 entity
Predicate keyPlayer P44793 FINISHED
Object Kiko Narváez
Kiko Narváez is a former Spanish footballer best known as a talented forward and playmaker for Atlético de Madrid and the Spanish national team in the 1990s.
E1596175 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: Kiko Narváez | Statement: [La Liga 1995-96 with Atlético de Madrid, keyPlayer, Kiko Narváez]
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: Kiko Narváez
Triple: [La Liga 1995-96 with Atlético de Madrid, keyPlayer, Kiko Narváez]
Generated description
Kiko Narváez is a former Spanish footballer best known as a talented forward and playmaker for Atlético de Madrid and the Spanish national team in the 1990s.

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_69e248f9e0a08190814772847003b1ff completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b091992c819085f8aa6cb91cb76a completed April 29, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f4582dcc4819083ec98bb15cd3b65 completed May 21, 2026, 5:48 p.m.
NEDg Description generation batch_6a0f47336054819084117d5f59c7b7df completed May 21, 2026, 5:56 p.m.
NED2 Entity disambiguation (via description) batch_6a0f48696d40819093e5fbeffa0b8925 completed May 21, 2026, 6:01 p.m.
Created at: April 17, 2026, 6:43 p.m.