Triple
T2254517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limoges |
E49689
|
entity |
| Predicate | regionSpeciality |
P21480
|
FINISHED |
| Object | Limoges porcelain |
—
|
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: Limoges porcelain | Statement: [Limoges, regionSpeciality, Limoges porcelain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionSpeciality Context triple: [Limoges, regionSpeciality, Limoges porcelain]
-
A.
regionSpecialization
chosen
Indicates that a region is designated or recognized as being particularly focused on, adapted to, or specialized in a specific function, activity, or domain.
-
B.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
C.
specialMunicipality
Indicates that an entity is designated as a special municipality, typically having a distinct administrative or legal status compared to regular municipalities.
-
D.
regionSpecificContent
Indicates that certain content is tailored, restricted, or applicable only to a particular geographic region or set of regions.
-
E.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc121af78819085b2e601d2f9bcdf |
completed | March 7, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69abbdb34c148190b51e99f540f97204 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.