Triple

T27981582
Position Surface form Disambiguated ID Type / Status
Subject Big Daddy Splash E706637 entity
Predicate signatureOf P150 FINISHED
Object Big Daddy
Big Daddy was a hugely popular British professional wrestler and television personality, best known for his larger-than-life persona and status as a household name in UK wrestling during the 1970s and 1980s.
E187396 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: Big Daddy | Statement: [Big Daddy Splash, signatureOf, Big Daddy]
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: Big Daddy
Triple: [Big Daddy Splash, signatureOf, Big Daddy]
Generated description
Big Daddy was a hugely popular British professional wrestler and television personality, best known for his larger-than-life persona and status as a household name in UK wrestling during the 1970s and 1980s.

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_69ef96b8b8d88190bad5e4ae966bf14e completed April 27, 2026, 5:02 p.m.
NER Named-entity recognition batch_69f63b693f448190b334a8a505aee5ad completed May 2, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15c8f495548190b62e660146962c5d completed May 26, 2026, 4:23 p.m.
NEDg Description generation batch_6a15ca1e992c819099d74611836ba016 completed May 26, 2026, 4:28 p.m.
NED2 Entity disambiguation (via description) batch_6a15cbdf46208190916381816f411f87 completed May 26, 2026, 4:35 p.m.
Created at: April 27, 2026, 7:44 p.m.