Triple
T36862602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biao people |
E910982
|
entity |
| Predicate | hasDemographicDistribution |
P114768
|
FINISHED |
| Object | primarily in Guangdong and Guangxi |
—
|
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: primarily in Guangdong and Guangxi | Statement: [Biao people, hasDemographicDistribution, primarily in Guangdong and Guangxi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDemographicDistribution Context triple: [Biao people, hasDemographicDistribution, primarily in Guangdong and Guangxi]
-
A.
hasDemographic
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
B.
hasDemographicPattern
Indicates that there is a characteristic distribution or trend of attributes (such as age, gender, income, or ethnicity) within a population or group.
-
C.
hasDemographicUnit
Indicates that an entity is associated with or contains a specific demographic unit (such as a population group or segment).
-
D.
hasDemographicPresenceIn
chosen
Indicates that a particular demographic group exists or is represented within a specified geographic area or population context.
-
E.
hasDemographicsCountry
Indicates that an entity’s demographic information is associated with or pertains to a specific country.
- 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_69f76e80f6f0819091cba8e19b269615 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff891e4b9c8190aa86a339a8944496 |
completed | May 9, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69ff8801180c8190b23e20996ca68e0a |
completed | May 9, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:13 p.m.