Triple
T21002129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Her Majesty's Secret Service |
E517318
|
entity |
| Predicate | employerOf |
P7
|
FINISHED |
| Object | Q |
—
|
NE NERFINISHED |
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: Q | Statement: [Her Majesty's Secret Service, employerOf, Q]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Q Context triple: [Her Majesty's Secret Service, employerOf, Q]
-
A.
Q
Q is a powerful, omnipotent trickster from the Q Continuum who frequently tests and torments the crew of the USS Enterprise in Star Trek: The Next Generation.
-
B.
Q
The Q is a New York City Subway service that runs along the BMT Broadway Line in Manhattan and the Brighton Line in Brooklyn, providing crosstown and interborough transit.
-
C.
Q
chosen
Q is a recurring comedic character from the James Bond film series, known as the eccentric head of MI6's gadget and technology division.
-
D.
Q
Q is the nickname of Anquan Boldin, a former NFL wide receiver known for his physical playing style and productivity with teams like the Arizona Cardinals and Baltimore Ravens.
-
E.
Q
Q refers to Qualcomm Stadium, the former multi-purpose sports venue in San Diego that hosted NFL and MLB teams as well as major events.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc25c1f8819086bdbfd89d390f5f |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:52 p.m.