Triple
T6195839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ürümqi |
E138505
|
entity |
| Predicate | languageTranscription |
P42524
|
FINISHED |
| Object | Chinese name 乌鲁木齐 |
—
|
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: Chinese name 乌鲁木齐 | Statement: [Ürümqi, languageTranscription, Chinese name 乌鲁木齐]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageTranscription Context triple: [Ürümqi, languageTranscription, Chinese name 乌鲁木齐]
-
A.
transcription
Indicates the process by which genetic information in DNA is copied into RNA by RNA polymerase.
-
B.
languageShift
Indicates a change in the primary language used by an entity, such as switching from one language to another over time or in a given context.
-
C.
translator
Indicates that one entity serves to convert or render content from one language or form into another for a second entity.
-
D.
transliterationLanguage
chosen
Indicates the language whose writing system is used as the target when converting text from one script to another.
-
E.
languageSpokenOnScreen
Indicates that a particular language is used in spoken dialogue or audible communication within an on-screen work (such as a film, show, or video).
- 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_69c008ab9b3081908a11b2c744838435 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0624571508190bd273b4a051fbe41 |
completed | March 22, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69c055fbce1081908805fd12e242ab96 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:20 p.m.