Triple
T3321548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Never Say Never Again |
E69805
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | M |
E109337
|
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: M | Statement: [Never Say Never Again, featuresCharacter, M]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: M Context triple: [Never Say Never Again, featuresCharacter, M]
-
A.
M
"M" is a 1951 American crime thriller film directed by Joseph Losey, adapted from Fritz Lang’s 1931 classic, in which David Wayne portrays a hunted child murderer.
-
B.
M
M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
-
C.
M
chosen
M is the codename for James Bond’s stern and authoritative superior who heads the British Secret Service in the 007 franchise.
-
D.
M
M is a New York City Subway service that runs along the IND Sixth Avenue Line in Manhattan and connects Brooklyn and Queens.
-
E.
Ma
Ma is a common Chinese surname borne by many notable individuals across fields such as music, politics, and sports.
- 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_69ad85a1829881908942c14075644d0d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb13b85208190b13aba355d5dafcf |
completed | March 8, 2026, 5:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a7954248190b0c7b5d6ab3c6687 |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:11 p.m.