Triple
T11115513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zyrtec |
E262874
|
entity |
| Predicate | foodInteraction |
P97018
|
FINISHED |
| Object | may be taken with or without food |
—
|
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: may be taken with or without food | Statement: [Zyrtec, foodInteraction, may be taken with or without food]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: foodInteraction Context triple: [Zyrtec, foodInteraction, may be taken with or without food]
-
A.
typicalFoodPairing
Indicates that one food item is commonly served, consumed, or matched together with another as a customary or complementary pairing.
-
B.
foodCustom
Indicates a culturally specific practice, rule, or tradition related to the preparation, serving, or consumption of food.
-
C.
foodItem
Indicates that one entity is a food item that can be eaten or used as food in relation to another entity.
-
D.
consequenceOfFood
Indicates a result, effect, or outcome that occurs because of a particular food or food-related action.
-
E.
foodEffect
Indicates how consuming a particular food influences or changes another entity, such as an organism, condition, or process.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79aa7254c8190abce35696ad2be03 |
completed | April 9, 2026, 12:25 p.m. |
| PD | Predicate disambiguation | batch_69d7441cf8188190b8095f622c923156 |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750cb26f48190a134614d0aa8e283 |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:27 p.m.