Triple
T19983706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Galbraith |
E493881
|
entity |
| Predicate | hasCoProtagonistOccupation |
P138179
|
FINISHED |
| Object | detective agency partner |
—
|
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: detective agency partner | Statement: [Robert Galbraith, hasCoProtagonistOccupation, detective agency partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCoProtagonistOccupation Context triple: [Robert Galbraith, hasCoProtagonistOccupation, detective agency partner]
-
A.
hasFictionalCoStar
Indicates that one entity appears as a co-star alongside another entity within a fictional work or narrative.
-
B.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
C.
hasSecondaryProtagonist
Indicates that an entity (such as a work of fiction) features another character who serves as a secondary or supporting main protagonist alongside the primary one.
-
D.
coProtagonist
Indicates that two or more entities share the primary leading role together in the same narrative work.
-
E.
hasTwinActors
Indicates that two or more actors share a twin relationship, typically portraying twin characters or being treated as twins within a given context.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d157d088190af861608936e59b7 |
completed | April 20, 2026, 5:06 p.m. |
| PD | Predicate disambiguation | batch_69e537fae79c81909eae39500766d0b6 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c42c688190a22f4d31ec692377 |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 11, 2026, 3:28 p.m.