Triple
T30000110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serra da Estrela cheese |
E762142
|
entity |
| Predicate | consistencyWhenYoung |
P91783
|
FINISHED |
| Object | soft |
—
|
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: soft | Statement: [Serra da Estrela cheese, consistencyWhenYoung, soft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: consistencyWhenYoung Context triple: [Serra da Estrela cheese, consistencyWhenYoung, soft]
-
A.
isYoung
Indicates that an entity is at an early stage of life or development relative to a relevant norm or comparison group.
-
B.
hasConsistency
chosen
Indicates that one entity maintains a stable, uniform, or coherent state, behavior, or set of properties in relation to another entity or over time.
-
C.
typicalConsistency
Indicates that one entity characteristically maintains a regular or expected level of consistency in relation to another entity or context.
-
D.
statusComparedToYoungGuard
Indicates how an entity’s status or standing compares relative to that of the young guard.
-
E.
youngerGenerationFluency
Indicates that members of a younger generation possess greater fluency (e.g., in a language, skill, or practice) compared to older generations.
- 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_69f2246a47ac81909cf5213053687ffc |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6794da2cc8190af2afa95c616305a |
completed | May 2, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 29, 2026, 6:41 p.m.