Triple
T36524787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Statesman whiskey |
E900271
|
entity |
| Predicate | settingOfFictionalDistillery |
P194394
|
FINISHED |
| Object | United States |
—
|
NE NERFINISHED |
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: United States | Statement: [Statesman whiskey, settingOfFictionalDistillery, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfFictionalDistillery Context triple: [Statesman whiskey, settingOfFictionalDistillery, United States]
-
A.
hasDistillery
Indicates that one entity owns, operates, or is associated with a distillery as a facility or production site.
-
B.
distilleryType
Indicates the specific kind or category of distillery associated with an entity (e.g., craft, industrial, micro, or regional type).
-
C.
hasNearbyDistillery
Indicates that one entity is located close to or in the vicinity of a distillery.
-
D.
isAlcoholicInFiction
Indicates that, within a fictional context, a character is portrayed as having alcoholism or a problematic dependence on alcohol.
-
E.
bartenderInFiction
Indicates that an entity serves as a bartender within a fictional work or narrative context.
- 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_69fd6dbd1b648190b1a0b391c03aebc5 |
completed | May 8, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69fd6a9020548190bbfa845360ac85fb |
completed | May 8, 2026, 4:46 a.m. |
| PDg | Predicate description generation | batch_69fd6dbc3ac0819093fbcfe95f12b93d |
completed | May 8, 2026, 4:59 a.m. |
Created at: May 3, 2026, 4:11 p.m.