Triple
T7588743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen's College, Hong Kong |
E179680
|
entity |
| Predicate | hasLanguageSubject |
P5462
|
FINISHED |
| Object | English Language |
—
|
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 Language | Statement: [Queen's College, Hong Kong, hasLanguageSubject, English Language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageSubject Context triple: [Queen's College, Hong Kong, hasLanguageSubject, English Language]
-
A.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
B.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
C.
hasLanguageAspect
Indicates that an entity is associated with a particular linguistic aspect, such as tense, mood, or grammatical feature, in relation to a language.
-
D.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
E.
hasLanguageOfStudy
chosen
Indicates that an entity studies or is engaged in learning a particular 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f99991948190af1fb0635895ad94 |
completed | March 27, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e04c2c8190a889d928515d9b8e |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:52 p.m.