Triple

T27218678
Position Surface form Disambiguated ID Type / Status
Subject Alan Carr E681207 entity
Predicate notableWork P4 FINISHED
Object There’s Something About Movies
There’s Something About Movies is a British comedy panel show about cinema, hosted by Alan Carr and featuring celebrity guests competing in film-themed games and quizzes.
 E1761040 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: There’s Something About Movies | Statement: [Alan Carr, notableWork, There’s Something About Movies]
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: There’s Something About Movies
Triple: [Alan Carr, notableWork, There’s Something About Movies]
Generated description
There’s Something About Movies is a British comedy panel show about cinema, hosted by Alan Carr and featuring celebrity guests competing in film-themed games and quizzes.

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_69eefac9f64c8190a07490fe0c8b72a3 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6261f30388190bd1643b2a61d2c5e completed May 2, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1253acb1b48190bbc6e9e09a4f17ad completed May 24, 2026, 1:26 a.m.
NEDg Description generation batch_6a1254e819d48190bfabeb72a073bac3 completed May 24, 2026, 1:31 a.m.
NED2 Entity disambiguation (via description) batch_6a12562f28108190bcba456c7c6b2429 completed May 24, 2026, 1:36 a.m.
Created at: April 27, 2026, 9:42 a.m.