Triple
T17863862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brevibacterium linens |
E446639
|
entity |
| Predicate | roleInFood |
P129066
|
FINISHED |
| Object | cheese ripening |
—
|
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: cheese ripening | Statement: [Brevibacterium linens, roleInFood, cheese ripening]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInFood Context triple: [Brevibacterium linens, roleInFood, cheese ripening]
-
A.
roleInCoco
Indicates that an entity serves a specific role or function within the context of the COCO dataset or framework.
-
B.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
C.
roleInVega
Indicates that an entity holds a specific role or function within the Vega context (such as the Vega project, system, or framework).
-
D.
usesFood
Indicates that one entity employs or consumes a food item as a resource, ingredient, or means to achieve some purpose.
-
E.
knownForDish
Indicates that an entity is recognized or notable for preparing, serving, or being associated with a particular dish.
- 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49791b0c08190b04a426bd274065d |
completed | April 19, 2026, 8:51 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e6d2e88190ad9ef9f8a99f13e6 |
completed | April 18, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69e3db7704588190a34a422421152173 |
completed | April 18, 2026, 7:28 p.m. |
Created at: April 10, 2026, 10:17 a.m.