Triple
T23959599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Cook a Pig |
E603887
|
entity |
| Predicate | hasIngredientFocus |
P5291
|
FINISHED |
| Object | pork |
—
|
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: pork | Statement: [How to Cook a Pig, hasIngredientFocus, pork]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIngredientFocus Context triple: [How to Cook a Pig, hasIngredientFocus, pork]
-
A.
hasMainIngredient
chosen
Indicates that one entity is the primary or most significant ingredient used to make another entity.
-
B.
usesIngredientInfluenceFrom
Indicates that one entity incorporates or applies the influence, properties, or effects derived from a particular ingredient in its action or outcome.
-
C.
usesIngredient
Indicates that one entity employs or incorporates another entity as an ingredient in its composition or creation.
-
D.
hasInactiveIngredient
Indicates that one entity contains another entity as a non-active (inactive) component or ingredient.
-
E.
hasDiningFocus
Indicates that an entity is primarily oriented toward or specialized in dining-related activities, services, or experiences.
- 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_69e2954222288190a7323554d0cca8d7 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d0d91cd481908f53ce7ee569c0f9 |
completed | April 29, 2026, 9:35 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:22 p.m.