Triple
T36524801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Statesman whiskey |
E900271
|
entity |
| Predicate | hasFictionalGeographicIdentity |
P201796
|
FINISHED |
| Object | Kentucky-style bourbon |
—
|
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: Kentucky-style bourbon | Statement: [Statesman whiskey, hasFictionalGeographicIdentity, Kentucky-style bourbon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalGeographicIdentity Context triple: [Statesman whiskey, hasFictionalGeographicIdentity, Kentucky-style bourbon]
-
A.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
B.
belongsToFictionalContinent
Indicates that something is located on, associated with, or a part of a specific fictional continent within an imagined world.
-
C.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
D.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
E.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a002249ee388190a9501ee7630dc658 |
completed | May 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_6a002189273881909b6b687e2d61f5b1 |
completed | May 10, 2026, 6:11 a.m. |
| PDg | Predicate description generation | batch_6a0022494be881908bdf945d2393a40f |
completed | May 10, 2026, 6:14 a.m. |
Created at: May 3, 2026, 4:11 p.m.