Triple
T12723967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avenue of Stars |
E304055
|
entity |
| Predicate | hasLanguageOnPlaques |
P67509
|
FINISHED |
| Object | 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: Chinese | Statement: [Avenue of Stars, hasLanguageOnPlaques, Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOnPlaques Context triple: [Avenue of Stars, hasLanguageOnPlaques, Chinese]
-
A.
hasLanguageOnPlaque
chosen
Indicates that a specific language appears in the text or inscription displayed on a particular plaque.
-
B.
hasCommemorativePlaques
Indicates that one entity possesses or displays commemorative plaques associated with it.
-
C.
inscriptionsLanguage
Indicates that the language used in the inscriptions on an object or surface is the specified language.
-
D.
officialLanguageOfSignage
Indicates that a particular language is the one officially used on public signs and signage within a given place or context.
-
E.
hasNumberOfNamesInscribed
Indicates the quantity of distinct names that are inscribed on a given 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96403957c81909acdee7bdae71696 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:25 p.m.