Triple
T16438900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Say Yes |
E399245
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Nathan Connolly |
E405704
|
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: Nathan Connolly | Statement: [Just Say Yes, composer, Nathan Connolly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nathan Connolly Context triple: [Just Say Yes, composer, Nathan Connolly]
-
A.
Nathan Connolly
Nathan Connolly is a contemporary writer best known for his work on the novel "Signal Fire."
-
B.
Nathan Connolly
chosen
Nathan Connolly is a Northern Irish musician best known as the lead guitarist and backing vocalist of the alternative rock band Snow Patrol.
-
C.
Ryan Connolly
Ryan Connolly is a filmmaker and YouTube personality best known for creating the popular filmmaking education channel Film Riot.
-
D.
Chris Connolly
Chris Connolly is a personal name shared by multiple individuals, including professionals in fields such as sports, media, and the arts.
-
E.
Luke Doolan
Luke Doolan is an Australian film editor and filmmaker best known for his work on acclaimed films such as "Animal Kingdom."
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32ba5a4748190b63ff53bfb5957c7 |
completed | April 18, 2026, 6:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007d9c8de4819093ae3901cf0c8805 |
completed | May 10, 2026, 12:44 p.m. |
Created at: April 10, 2026, 5:10 a.m.