Triple
T29313103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Csíkszentmárton |
E743299
|
entity |
| Predicate | hasLocallyUsedLanguage |
P73137
|
FINISHED |
| Object | Hungarian |
—
|
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: Hungarian | Statement: [Csíkszentmárton, hasLocallyUsedLanguage, Hungarian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocallyUsedLanguage Context triple: [Csíkszentmárton, hasLocallyUsedLanguage, Hungarian]
-
A.
hasLocalServicesLanguage
Indicates that the local services available in a given context operate or are provided using a specified language.
-
B.
usesLocalLanguageVariant
chosen
Indicates that an entity employs a region-specific or localized form of a language rather than a standard or global variant.
-
C.
hasPrimaryLanguageNearby
Indicates that an entity is associated with a primary language that is predominantly used or present in its immediate geographic or contextual vicinity.
-
D.
hasStandardLanguageNearby
Indicates that a standard or commonly used language is present in close proximity to the referenced entity.
-
E.
mayUseLanguagesOf
Indicates that an entity is permitted to use the languages associated with another entity.
- 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_69f0912502c8819087d9e8398ee991a8 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: April 28, 2026, 1:18 p.m.