Triple
T27131367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Happy Meal |
E681570
|
entity |
| Predicate | typicalDrinkOption |
P200236
|
FINISHED |
| Object | soft drink |
—
|
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: soft drink | Statement: [Happy Meal, typicalDrinkOption, soft drink]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDrinkOption Context triple: [Happy Meal, typicalDrinkOption, soft drink]
-
A.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
B.
drinksWith
Indicates that two entities consume beverages together, typically at the same time and place in a social context.
-
C.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
-
D.
favoriteDrink
Indicates that one entity has a preferred beverage over others.
-
E.
drinkFamily
Indicates a familial or close relational connection between two entities centered around drinking-related activities or contexts.
- 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_69eefacbcc2081909ebf00daa23f1981 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69ff7c96e6dc8190b89554480ebcea39 |
completed | May 9, 2026, 6:27 p.m. |
| PD | Predicate disambiguation | batch_69ff7c2381748190ad9a2176e0e478cd |
completed | May 9, 2026, 6:25 p.m. |
| PDg | Predicate description generation | batch_69ff7c96036c8190bfae5ec9b5a1965f |
completed | May 9, 2026, 6:27 p.m. |
Created at: April 27, 2026, 9:04 a.m.