Triple
T35210334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangzhouhua |
E1016655
|
entity |
| Predicate | typicalSpeakerIs |
P3327
|
FINISHED |
| Object | bilingual in Hangzhouhua and Standard Mandarin |
—
|
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: bilingual in Hangzhouhua and Standard Mandarin | Statement: [Hangzhouhua, typicalSpeakerIs, bilingual in Hangzhouhua and Standard Mandarin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpeakerIs Context triple: [Hangzhouhua, typicalSpeakerIs, bilingual in Hangzhouhua and Standard Mandarin]
-
A.
typicalSpeaker
chosen
Indicates that the subject is a prototypical or characteristic speaker or source of utterances in the context of the object.
-
B.
typicalSpeakersAre
Indicates that the entities specified are the kinds of speakers who typically use or produce the expression or language in question.
-
C.
typicalRole
Indicates that one entity serves as the usual, characteristic, or commonly expected role or function of another entity.
-
D.
commonsSpeaker
Indicates that a person serves as the Speaker (presiding officer) of the House of Commons.
-
E.
typicalProfile
Indicates that an entity represents the standard or most representative profile or pattern for another 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_69f76ddf549c8190869d0af076fd2c28 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:02 p.m.