Triple
T31353120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dale Cooper |
E799650
|
entity |
| Predicate | favoriteBeverage |
P8713
|
FINISHED |
| Object | black coffee |
—
|
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: black coffee | Statement: [Dale Cooper, favoriteBeverage, black coffee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: favoriteBeverage Context triple: [Dale Cooper, favoriteBeverage, black coffee]
-
A.
favoriteDrink
chosen
Indicates that one entity has a preferred beverage over others.
-
B.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
C.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
-
D.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
E.
favoriteFood
Indicates that one entity has a preferred or most liked food item in relation to another entity or context.
- 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_69f224e5e9bc8190a16339328897c4f8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69f431c5081908fe6506e34d1ecb5 |
completed | May 3, 2026, 1:05 a.m. |
| PD | Predicate disambiguation | batch_69f69d1d25e88190a7f57d323574da90 |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 9:17 p.m.