Triple

T26667691
Position Surface form Disambiguated ID Type / Status
Subject Montpellier Handball E672230 entity
Predicate notablePlayer P304 FINISHED
Object Thierry Omeyer
Thierry Omeyer is a legendary French handball goalkeeper, widely regarded as one of the greatest in the sport’s history and a multiple world and Olympic champion with France.
E2033517 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: Thierry Omeyer | Statement: [Montpellier Handball, notablePlayer, Thierry Omeyer]
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: Thierry Omeyer
Triple: [Montpellier Handball, notablePlayer, Thierry Omeyer]
Generated description
Thierry Omeyer is a legendary French handball goalkeeper, widely regarded as one of the greatest in the sport’s history and a multiple world and Olympic champion with France.

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_69eecda00a9c8190b2691f4d89db03b6 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f616c4dda8819088c175f27c5f0961 completed May 2, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a34e4e3ff988190b4c6a2d4cd0b3f14 completed June 19, 2026, 6:42 a.m.
NEDg Description generation batch_6a34e5b49a648190aca8b4ea64bb77da completed June 19, 2026, 6:46 a.m.
NED2 Entity disambiguation (via description) batch_6a34e66fc7fc81908758e4f67ce150b4 completed June 19, 2026, 6:49 a.m.
Created at: April 27, 2026, 3:11 a.m.