Triple

T27580294
Position Surface form Disambiguated ID Type / Status
Subject La Rojita E699565 entity
Predicate notableAlumni P51 FINISHED
Object Juan Mata
Juan Mata is a Spanish professional footballer known for his creative playmaking as an attacking midfielder for clubs like Valencia, Chelsea, and Manchester United, as well as for Spain’s national team.
E1779960 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: Juan Mata | Statement: [La Rojita, notableAlumni, Juan Mata]
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: Juan Mata
Triple: [La Rojita, notableAlumni, Juan Mata]
Generated description
Juan Mata is a Spanish professional footballer known for his creative playmaking as an attacking midfielder for clubs like Valencia, Chelsea, and Manchester United, as well as for Spain’s national team.

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_69ef6a4cb8b881909b3a8d630fd89df2 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f63015a6bc8190b4f47236dd4b6680 completed May 2, 2026, 5:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0d5fa90819080d04891930d10e8 completed May 24, 2026, 10:20 a.m.
NEDg Description generation batch_6a12d18be6088190a4662ba4460dfc84 completed May 24, 2026, 10:23 a.m.
NED2 Entity disambiguation (via description) batch_6a12d26af1288190a2925d726ab5be31 completed May 24, 2026, 10:26 a.m.
Created at: April 27, 2026, 2:02 p.m.