Triple
T37138166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Jeffrey Geiger |
E920028
|
entity |
| Predicate | isCharacterOn |
P176973
|
FINISHED |
| Object | medical drama television series |
—
|
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: medical drama television series | Statement: [Dr. Jeffrey Geiger, isCharacterOn, medical drama television series]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isCharacterOn Context triple: [Dr. Jeffrey Geiger, isCharacterOn, medical drama television series]
-
A.
hasCharacterPresence
Indicates that a particular character appears or is present within a specified context, such as a scene, work, or medium.
-
B.
hasCharacterOnboard
Indicates that a vehicle, vessel, or similar entity currently has a specific character present on board.
-
C.
isCharacterInSetting
Indicates that a particular character appears or exists within a specified setting or environment.
-
D.
meetsCharacterAtLocation
Indicates that one character encounters or comes together with another character at a specific location.
-
E.
isCharacterInWork
chosen
Indicates that a particular character appears in or is part of a specified creative work (such as a book, film, or game).
- 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_69f76e9e9d008190a250b0387c992c74 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:15 p.m.