Triple

T34090247
Position Surface form Disambiguated ID Type / Status
Subject Gaizka Mendieta E874283 entity
Predicate awardReceived P11 FINISHED
Object UEFA Best Midfielder Award
The UEFA Best Midfielder Award is a European football honor presented by UEFA to recognize the most outstanding midfield player in its club competitions for a given season.
E2081099 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: UEFA Best Midfielder Award | Statement: [Gaizka Mendieta, awardReceived, UEFA Best Midfielder Award]
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: UEFA Best Midfielder Award
Triple: [Gaizka Mendieta, awardReceived, UEFA Best Midfielder Award]
Generated description
The UEFA Best Midfielder Award is a European football honor presented by UEFA to recognize the most outstanding midfield player in its club competitions for a given season.

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_69f349a61d448190b74642f325d3eb7a completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f70c12b388819080f5e15aee998d77 completed May 3, 2026, 8:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36ae5c3e2c8190a64af10f5c848f3b completed June 20, 2026, 3:14 p.m.
NEDg Description generation batch_6a36af3eb7288190bee994ee99c9cb56 completed June 20, 2026, 3:18 p.m.
NED2 Entity disambiguation (via description) batch_6a36afe7a9208190952f11924f15856b completed June 20, 2026, 3:21 p.m.
Created at: May 1, 2026, 1:52 a.m.