Triple
T13021433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smile Please |
E326179
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Malcolm Cecil |
E325976
|
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: Malcolm Cecil | Statement: [Smile Please, producer, Malcolm Cecil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malcolm Cecil Context triple: [Smile Please, producer, Malcolm Cecil]
-
A.
Malcolm Cecil
chosen
Malcolm Cecil was a pioneering British musician, producer, and electronic music innovator best known for co-creating the massive TONTO synthesizer and shaping the sound of early 1970s Stevie Wonder albums.
-
B.
Malcolm McDonald
Malcolm McDonald is a British marketing scholar and author renowned for his influential work on marketing planning and strategy.
-
C.
George Morrison
George Morrison is a notable individual whose name is shared with several prominent figures across fields such as politics, art, and entertainment.
-
D.
Roy Marples
Roy Marples is a software engineer best known for his work on the OpenRC init system and various networking tools in the Linux and BSD ecosystems.
-
E.
Malcolm Cooke
Malcolm Cooke is a film editor known for his work on the 1986 monster movie "King Kong Lives."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecf21bc819082fb512bc479b4be |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c119e19c81908ae2b1caff6f2f32 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:52 p.m.