Triple
T27624946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhangjiajie West railway station |
E696175
|
entity |
| Predicate | hasNativeLanguageSignage |
P4196
|
FINISHED |
| Object | Simplified Chinese |
—
|
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: Simplified Chinese | Statement: [Zhangjiajie West railway station, hasNativeLanguageSignage, Simplified Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNativeLanguageSignage Context triple: [Zhangjiajie West railway station, hasNativeLanguageSignage, Simplified Chinese]
-
A.
hasEnglishSignage
Indicates that the subject features signs or written information presented in the English language.
-
B.
officialLanguageOfSignage
Indicates that a particular language is the one officially used on public signs and signage within a given place or context.
-
C.
hasAdditionalLanguageOfSignage
Indicates that an entity has signage presented in one or more additional languages beyond the primary language used.
-
D.
languageOfSignage
chosen
Indicates the language used on signs or written displays associated with an entity.
-
E.
hasIrishLanguageSignage
Indicates that signage associated with an entity includes text in the Irish 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_69ef59092c8881908114ad184248cc46 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: April 27, 2026, 2:17 p.m.