Triple
T32631546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angela Lewis |
E834223
|
entity |
| Predicate | roleInWorkplaceDynamics |
P201992
|
FINISHED |
| Object | colleague of Marcus Graham |
—
|
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: colleague of Marcus Graham | Statement: [Angela Lewis, roleInWorkplaceDynamics, colleague of Marcus Graham]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInWorkplaceDynamics Context triple: [Angela Lewis, roleInWorkplaceDynamics, colleague of Marcus Graham]
-
A.
roleInPersonnelMatters
Indicates that one entity has a specific function, authority, or involvement in managing or deciding personnel-related matters concerning another entity.
-
B.
roleForWork
Indicates that a particular role or position is assigned or designated for performing a specific work, task, or job.
-
C.
inUniverseWorkplaceRole
Indicates that an entity holds a specific workplace role or job position within a fictional or narrative universe.
-
D.
roleSynergy
Indicates how effectively two or more roles complement and enhance each other’s performance when combined.
-
E.
roleInHumanFlow
Indicates that an entity participates in the movie *Human Flow* in a specific role (such as director, actor, or producer).
- 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_69f3492dc2308190a88c6e30a3f3f576 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a0041860fd081908623ef9fd40a9775 |
completed | May 10, 2026, 8:27 a.m. |
| PD | Predicate disambiguation | batch_6a00414b01488190a312adb3b25d69cf |
completed | May 10, 2026, 8:26 a.m. |
| PDg | Predicate description generation | batch_6a00418517008190b33399a08a003ee2 |
completed | May 10, 2026, 8:27 a.m. |
Created at: May 1, 2026, 1:07 a.m.