Triple
T34847629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poker Flat, California |
E1004513
|
entity |
| Predicate | hasWorkAsSettingType |
P78942
|
FINISHED |
| Object | short story |
—
|
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: short story | Statement: [Poker Flat, California, hasWorkAsSettingType, short story]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkAsSettingType Context triple: [Poker Flat, California, hasWorkAsSettingType, short story]
-
A.
hasWorkAsSetting
chosen
Indicates that a particular work (such as a story, film, or artwork) takes place in or uses a specified location, time, or environment as its setting.
-
B.
hasWorkTypeRelation
Indicates a relationship specifying the type or category of work associated with an entity.
-
C.
hasSignificantWorkType
Indicates that an entity is associated with a primary or notably important type or category of work.
-
D.
workTypeOfSetting
Indicates the type or nature of work activity that characterizes or occurs within a particular setting.
-
E.
hasWorkField
Indicates that an entity is associated with or operates within a particular field or area of work.
- 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_69f76dba76f0819090643cba102c41ec |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fe610e1f6881908f10070ba64643cf |
completed | May 8, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69fe604c6c008190ad659e9b9fa82f7b |
completed | May 8, 2026, 10:14 p.m. |
Created at: May 3, 2026, 4 p.m.