Triple
T37142149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abondance Valley |
E920141
|
entity |
| Predicate | cheeseNameOrigin |
P50213
|
FINISHED |
| Object | Abondance cheese |
—
|
NE NERFINISHED |
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: Abondance cheese | Statement: [Abondance Valley, cheeseNameOrigin, Abondance cheese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cheeseNameOrigin Context triple: [Abondance Valley, cheeseNameOrigin, Abondance cheese]
-
A.
cheeseMarketTraditionSince
Indicates that a cheese market tradition has been in existence or practiced since a specified point in time.
-
B.
cheeseMadeFrom
Indicates that one entity is produced or derived as cheese from another entity (typically a source ingredient such as milk).
-
C.
hasCheeseStyleOrigin
Indicates that a cheese’s style or type originates from, or is traditionally associated with, a particular place or region.
-
D.
cheeseSpeciality
Indicates that one entity is known for or specializes in producing or offering a particular type of cheese.
-
E.
hasCheeseVarietyNamedAfterIt
chosen
Indicates that something has a type or variety of cheese that is named after it.
- 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_69f76e9e9d008190a250b0387c992c74 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:15 p.m.