Triple
T10470475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomorrow Never Dies |
E246909
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Samantha Bond |
E424282
|
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: Samantha Bond | Statement: [Tomorrow Never Dies, starring, Samantha Bond]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samantha Bond Context triple: [Tomorrow Never Dies, starring, Samantha Bond]
-
A.
Samantha Bond
chosen
Samantha Bond is an English actress best known for playing Miss Moneypenny in the James Bond film series during the Pierce Brosnan era.
-
B.
Samantha Winslow
Samantha Winslow is an American photographer and the wife of renowned film composer John Williams.
-
C.
Samantha Noble
Samantha Noble is the daughter of Australian actor John Noble.
-
D.
Ann Bond
Ann Bond is a relatively obscure individual known primarily for sharing the surname associated with the famous fictional spy James Bond.
-
E.
Samantha Wheeler
Samantha Wheeler is a formidable and ambitious corporate lawyer introduced in later seasons of the TV series "Suits," known for her sharp legal skills and fierce loyalty.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509305fec81908b1acd91ae1f875d |
completed | April 7, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dc68b49481909715c36a4c0e7c4f |
completed | April 10, 2026, 11:18 a.m. |
Created at: April 6, 2026, 12:20 p.m.