Triple
T8538082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raoul Silva |
E202126
|
entity |
| Predicate | capturedBy |
P4712
|
FINISHED |
| Object | James Bond |
E18670
|
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: James Bond | Statement: [Raoul Silva, capturedBy, James Bond]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: James Bond Context triple: [Raoul Silva, capturedBy, James Bond]
-
A.
James Bond
chosen
James Bond is a fictional British secret agent, code-named 007, known for his espionage missions, suave demeanor, and presence in a long-running series of novels and films.
-
B.
Jack Bond
Jack Bond is a British film and television director known for his work on music videos and collaborations with prominent artists.
-
C.
JC Bond
JC Bond is a film editor known for working on the movie "Big Eyes."
-
D.
Q (James Bond)
Q is the fictional head of Q Branch in the James Bond series, known for equipping 007 with advanced gadgets and technology.
-
E.
John Bond (police officer)
John Bond is a British forensic scientist and police officer known for pioneering techniques to recover fingerprints from spent bullet casings and other challenging surfaces.
- 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_69ca832355b08190b8b6a4ab4a4a3554 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6a5c2848190836c612597de83b7 |
completed | March 31, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce890eb0b48190aa76cc955d00ec18 |
completed | April 2, 2026, 3:19 p.m. |
Created at: March 30, 2026, 6:18 p.m.