Triple
T25052869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeeves and the Tie That Binds |
E627431
|
entity |
| Predicate | otherProtagonistOccupation |
P158712
|
FINISHED |
| Object | valet |
—
|
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: valet | Statement: [Jeeves and the Tie That Binds, otherProtagonistOccupation, valet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherProtagonistOccupation Context triple: [Jeeves and the Tie That Binds, otherProtagonistOccupation, valet]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
hasCoProtagonistOccupation
Indicates that two or more co-protagonists share a specified occupation or professional role.
-
C.
portrayedProfessionOfCharacter
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
-
D.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
E.
laterOccupationInFiction
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
- F. None of above. chosen
Provenance (4 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_69e2ff2c45f48190afa28369f1df6786 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f47b865df48190bf4b6d3e9f9305e6 |
completed | May 1, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69f4682c8a3c8190adbfaac99474eaaf |
completed | May 1, 2026, 8:45 a.m. |
| PDg | Predicate description generation | batch_69f47b7f657c81908174590c811a3cbf |
completed | May 1, 2026, 10:07 a.m. |
Created at: April 18, 2026, 6:09 a.m.