Triple
T684579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonathan Nolan |
E13256
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Nolan |
E48157
|
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: Nolan | Statement: [Jonathan Nolan, familyName, Nolan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nolan Context triple: [Jonathan Nolan, familyName, Nolan]
-
A.
Nolan
chosen
Nolan is a common Irish surname that has been borne by numerous notable figures across fields such as film, sports, and politics.
-
B.
Christopher Nolan
Christopher Nolan is a British-American filmmaker renowned for his intellectually ambitious, visually striking blockbusters such as Inception, The Dark Knight trilogy, and Interstellar.
-
C.
Snyder
Snyder is a surname most prominently associated with Dan Snyder, the American businessman and former owner of the NFL’s Washington Commanders.
-
D.
Neil
Neil is the given name of Neil deGrasse Tyson, a prominent American astrophysicist, author, and science communicator.
-
E.
Don
Don is a masculine given name, often a short form of Donald, used in English-speaking countries.
- 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0725c708190aa6edfee742ca4e6 |
completed | March 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a6374ee9ac819091abef4167e3433e |
completed | March 3, 2026, 1:20 a.m. |
Created at: March 1, 2026, 7:36 p.m.