Triple

T37564069
Position Surface form Disambiguated ID Type / Status
Subject Saint and Greavsie E933903 entity
Predicate presenter P83 FINISHED
Object Ian St John
Ian St John was a Scottish footballer and television pundit best known as a prolific Liverpool forward in the 1960s and later as co-host of the popular British football show "Saint and Greavsie."
E2235894 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: Ian St John | Statement: [Saint and Greavsie, presenter, Ian St John]
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: Ian St John
Triple: [Saint and Greavsie, presenter, Ian St John]
Generated description
Ian St John was a Scottish footballer and television pundit best known as a prolific Liverpool forward in the 1960s and later as co-host of the popular British football show "Saint and Greavsie."

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_69f76ecb4acc8190b53f96d0b013e415 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba481cb588190b56a39288a915044 completed May 6, 2026, 8:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40afd6988c8190b85312d9e938ec83 completed June 28, 2026, 5:23 a.m.
NEDg Description generation batch_6a40b0b1e9f48190a837ca9e2b33f35a completed June 28, 2026, 5:27 a.m.
NED2 Entity disambiguation (via description) batch_6a40b10e1c7881909c83962729029f0a completed June 28, 2026, 5:28 a.m.
Created at: May 3, 2026, 4:17 p.m.