Triple

T25613083
Position Surface form Disambiguated ID Type / Status
Subject Newcastle Falcons E642087 entity
Predicate notablePlayer P304 FINISHED
Object Toby Flood
Toby Flood is an English former rugby union fly-half and centre who played for clubs including Leicester Tigers and Toulouse and earned numerous caps for the England national team.
E1686509 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: Toby Flood | Statement: [Newcastle Falcons, notablePlayer, Toby Flood]
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: Toby Flood
Triple: [Newcastle Falcons, notablePlayer, Toby Flood]
Generated description
Toby Flood is an English former rugby union fly-half and centre who played for clubs including Leicester Tigers and Toulouse and earned numerous caps for the England 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_69e75dc6ccf081908d49578fd36a76d5 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f9e447048190bdda16b12f309740 completed May 2, 2026, 1:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10b77986e08190901250a9b6746f55 completed May 22, 2026, 8:07 p.m.
NEDg Description generation batch_6a10b84ad3ac8190b78bec4cd68a84e8 completed May 22, 2026, 8:10 p.m.
NED2 Entity disambiguation (via description) batch_6a10b97dedd48190858687f050f15f7b completed May 22, 2026, 8:15 p.m.
Created at: April 21, 2026, 4:42 p.m.