Triple
T28531250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milk |
E722046
|
entity |
| Predicate | homogenization |
P97243
|
FINISHED |
| Object | Often homogenized to prevent cream separation |
—
|
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: Often homogenized to prevent cream separation | Statement: [Milk, homogenization, Often homogenized to prevent cream separation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: homogenization Context triple: [Milk, homogenization, Often homogenized to prevent cream separation]
-
A.
uniformizes
chosen
Indicates making multiple entities or elements consistent, standardized, or uniform in form, appearance, or behavior.
-
B.
homologationFor
Indicates that something is formally approved, certified, or validated for a specific use, standard, or regulatory context.
-
C.
concentrates
Indicates that one entity directs its attention, effort, or resources intensely toward a specific target, task, or area.
-
D.
reduction
Indicates a relationship where something is decreased in amount, size, intensity, or degree compared to a prior state or reference.
-
E.
generalizationOf
Indicates that one entity represents a broader, more general concept or category that subsumes or abstracts over another, more specific 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_69f01a5d7ec88190ada2d5be7c06c35d |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f64fd617588190904f94042d6f3eb1 |
completed | May 2, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 3:28 a.m.