Triple
T37138723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Staff Sergeant Wilhelm |
E920044
|
entity |
| Predicate | notableSceneSetting |
P128577
|
FINISHED |
| Object | French tavern |
—
|
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: French tavern | Statement: [Staff Sergeant Wilhelm, notableSceneSetting, French tavern]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableSceneSetting Context triple: [Staff Sergeant Wilhelm, notableSceneSetting, French tavern]
-
A.
notableScene
Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
-
B.
notableSceneProp
Indicates that an object or element serves as a significant or prominently featured prop within a particular scene.
-
C.
notableSceneAssociation
chosen
Indicates an association between an entity and a notable or memorable scene in which it prominently appears or plays a significant role.
-
D.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
E.
notableAsSettingOf
Indicates that a place or environment is recognized as the setting where the events of a particular work (e.g., book, film, story) take place.
- 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_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:15 p.m.