Triple
T10557189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Nolan (academic) |
E249117
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Paul Nolan |
E249117
|
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: Paul Nolan | Statement: [Paul Nolan (academic), name, Paul Nolan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Nolan Context triple: [Paul Nolan (academic), name, Paul Nolan]
-
A.
Paul Nolan
chosen
Paul Nolan is a relatively common personal name shared by various individuals, including professionals in fields such as sports, entertainment, and academia.
-
B.
Robert Nolan
Robert Nolan is a relatively common personal name shared by multiple individuals across various professions, including arts, sports, and public life.
-
C.
Dick Nolan
Dick Nolan was an American football player and longtime NFL head coach, best known for leading the San Francisco 49ers in the late 1960s and early 1970s.
-
D.
Lloyd Nolan
Lloyd Nolan was an American film and television actor known for his versatile character roles in dramas, crime films, and later in popular TV series.
-
E.
William Nolan
William Nolan is an editor known for his work on editions of classic adventure literature, including "The Mark of Zorro."
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d52713eaa48190936b4b15e2c7e827 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbacc05334819081e994d75b5e9318 |
completed | April 12, 2026, 2:31 p.m. |
Created at: April 6, 2026, 12:35 p.m.