Triple
T12245476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Town, Grand Cayman |
E291839
|
entity |
| Predicate | languageDeFacto |
P237
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [George Town, Grand Cayman, languageDeFacto, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageDeFacto Context triple: [George Town, Grand Cayman, languageDeFacto, English]
-
A.
deFactoLanguage
chosen
Indicates that a language is used in practice as the primary or common language in a context, even if it has no official legal status there.
-
B.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
C.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
D.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
-
E.
languageIndependence
Indicates that a concept, method, or representation does not depend on any specific programming or natural language and can be applied uniformly across different languages.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91d38ee10819093ed41d2954bf4ef |
completed | April 10, 2026, 3:54 p.m. |
| PD | Predicate disambiguation | batch_69d91c46dcd88190a263db30804bff36 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.