Triple
T24432404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lifou people |
E616032
|
entity |
| Predicate | demographicArea |
P105946
|
FINISHED |
| Object | commune of Lifou |
—
|
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: commune of Lifou | Statement: [Lifou people, demographicArea, commune of Lifou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicArea Context triple: [Lifou people, demographicArea, commune of Lifou]
-
A.
demographicRegion
Indicates that an entity is associated with, belongs to, or is characterized by a particular geographic or administrative region for demographic purposes.
-
B.
statisticalAreaOf
chosen
Indicates that one entity is the designated statistical area or region associated with, containing, or characterizing another entity for statistical or demographic purposes.
-
C.
arealRegion
Indicates that something occupies or pertains to a specific two-dimensional geographic or spatial area.
-
D.
demographicBasis
Indicates that something is determined, classified, or justified based on demographic characteristics such as age, gender, ethnicity, or similar population attributes.
-
E.
demographicScope
Indicates the specific population group or demographic segment to which something (e.g., a policy, study, product, or service) is targeted or applicable.
- 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_69e2d7ec44b081909ccaf1f3bbec0641 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f29783b3208190997c47be1aa229af |
completed | April 29, 2026, 11:42 p.m. |
| PD | Predicate disambiguation | batch_69f287d3237c819099559c00f83131d8 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:16 a.m.