Triple
T3934370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Hennings |
E90873
|
entity |
| Predicate | roleInHorribleBosses |
P52629
|
FINISHED |
| Object | director of photography |
—
|
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: director of photography | Statement: [David Hennings, roleInHorribleBosses, director of photography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInHorribleBosses Context triple: [David Hennings, roleInHorribleBosses, director of photography]
-
A.
antagonistOccupation
Indicates the role, job, or professional activity that the antagonist character performs.
-
B.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
C.
roleInTheology
Indicates the specific function, position, or significance an entity holds within a theological system, doctrine, or belief framework.
-
D.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
E.
deFactoRole
Indicates that an entity effectively functions in a role or capacity in practice, even if that role is not formally or officially assigned.
- 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_69aed95f26e0819094b0e71974543a19 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeedcbf0188190a5e828707a77752a |
completed | March 9, 2026, 3:57 p.m. |
| PD | Predicate disambiguation | batch_69aee7625ad4819097e4e8a168c19274 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeed3260cc8190bf294bdab507b1f9 |
completed | March 9, 2026, 3:54 p.m. |
Created at: March 9, 2026, 3:23 p.m.