Triple
T1013934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | On Her Majesty's Secret Service |
E21887
|
entity |
| Predicate | featuresRelationship |
P37
|
FINISHED |
| Object | James Bond and Tracy di Vicenzo |
—
|
LITERAL 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 and Tracy di Vicenzo | Statement: [On Her Majesty's Secret Service, featuresRelationship, James Bond and Tracy di Vicenzo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresRelationship Context triple: [On Her Majesty's Secret Service, featuresRelationship, James Bond and Tracy di Vicenzo]
-
A.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
semanticRelation
Indicates a general meaning-based connection between two entities, such as similarity, implication, or conceptual association.
-
C.
relatedTo
chosen
Indicates a general, non-specific relationship or association exists between two entities.
-
D.
relatedField
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
E.
definesRelationshipBetween
Indicates that one entity specifies or establishes the nature, type, or rules of a relationship that exists between two or more other entities.
- F. None of above.
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_69a493c68e24819080ed0ee8bcfd5ce0 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7be907c8190b5c6ea89257755a7 |
completed | March 1, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69a4b72207c08190a3dbb2aa7acbbc71 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.