Triple
T5526296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mont d'Or cheese |
E144929
|
entity |
| Predicate | typicalConsistencyWhenRipe |
P47122
|
FINISHED |
| Object | spoonable |
—
|
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: spoonable | Statement: [Mont d'Or cheese, typicalConsistencyWhenRipe, spoonable]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalConsistencyWhenRipe Context triple: [Mont d'Or cheese, typicalConsistencyWhenRipe, spoonable]
-
A.
ripeningTime
Indicates the period or duration required for something to become fully ripe or reach its mature, ready-to-use state.
-
B.
typicalConsistency
chosen
Indicates that one entity characteristically maintains a regular or expected level of consistency in relation to another entity or context.
-
C.
typicalSauceConsistency
Indicates that something has the usual or characteristic thickness or texture expected of a sauce.
-
D.
typicalSweetnessLevel
Indicates the usual or characteristic degree of sweetness associated with something.
-
E.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
- 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f89112c8190b276317fcc2acce0 |
completed | March 22, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_69c01b0a06348190b39ac9fe80d2836a |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:34 p.m.