Triple
T16600699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penne |
E403321
|
entity |
| Predicate | localLanguageOrDialect |
P115774
|
FINISHED |
| Object | Abruzzese dialect |
—
|
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: Abruzzese dialect | Statement: [Penne, localLanguageOrDialect, Abruzzese dialect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localLanguageOrDialect Context triple: [Penne, localLanguageOrDialect, Abruzzese dialect]
-
A.
localLanguageName
Indicates the name of a language as it is written or referred to in its own local or native form.
-
B.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
C.
languageUsedInLocality
chosen
Indicates that a particular language is used or spoken within a specific locality or geographic area.
-
D.
primaryLocalLanguageFamily
Indicates the main linguistic family to which the predominant local language of an entity belongs.
-
E.
nativeLanguage
Indicates the language that a person or entity originally learned and uses as their primary or first language.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e35d764574819081366374ca0c9bea |
completed | April 18, 2026, 10:31 a.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.