Triple
T18316613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tex-Mex tacos |
E438765
|
entity |
| Predicate | usesMeatPreparation |
P40977
|
FINISHED |
| Object | seasoned ground beef |
—
|
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: seasoned ground beef | Statement: [Tex-Mex tacos, usesMeatPreparation, seasoned ground beef]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMeatPreparation Context triple: [Tex-Mex tacos, usesMeatPreparation, seasoned ground beef]
-
A.
meatPreparation
chosen
Indicates the method or process by which meat is treated, cooked, or otherwise prepared for consumption.
-
B.
meatType
Indicates the specific category or kind of meat associated with an entity.
-
C.
isUsuallyCookedIn
Indicates that something is most commonly or typically prepared or cooked within a particular container, appliance, or environment.
-
D.
typicalMeat
Indicates that something is commonly or characteristically used or regarded as meat in a given context.
-
E.
notableMeatProduct
Indicates that one entity is a meat-based product that is especially prominent, well-known, or significant in relation to the other entity.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021e61008190a300b6c51976a837 |
completed | April 19, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69e44fe4ee10819086b4142444fca1f5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.