Triple
T34847650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poker Flat, California |
E1004513
|
entity |
| Predicate | hasFictionalInhabitantsType |
P97696
|
FINISHED |
| Object | gamblers |
—
|
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: gamblers | Statement: [Poker Flat, California, hasFictionalInhabitantsType, gamblers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalInhabitantsType Context triple: [Poker Flat, California, hasFictionalInhabitantsType, gamblers]
-
A.
hasFictionalInhabitants
chosen
Indicates that a place or setting is inhabited by fictional or imaginary beings.
-
B.
hasFictionalEstablishmentType
Indicates that an establishment is associated with a particular type or category of fictional setting or institution.
-
C.
hasFictionalWorldType
Indicates that an entity is associated with, set in, or characterized by a particular type or category of fictional world.
-
D.
hasFictionalUniverseElement
Indicates that one entity is a component, feature, or constituent part of the fictional universe represented by the other entity.
-
E.
hasSpeciesInFiction
Indicates that a fictional work or universe features a particular species as part of its narrative or setting.
- 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_69ffa15d53208190ab8574d6c7913e18 |
completed | May 9, 2026, 9:04 p.m. |
| PD | Predicate disambiguation | batch_69ff9eee681c81909434e79c627cb528 |
completed | May 9, 2026, 8:54 p.m. |
Created at: May 3, 2026, 4 p.m.