Triple
T22870330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bordeaux |
E567173
|
entity |
| Predicate | hasUrbanAreaPopulationApprox |
P1070
|
FINISHED |
| Object | around 1 million inhabitants |
—
|
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: around 1 million inhabitants | Statement: [Bordeaux, hasUrbanAreaPopulationApprox, around 1 million inhabitants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanAreaPopulationApprox Context triple: [Bordeaux, hasUrbanAreaPopulationApprox, around 1 million inhabitants]
-
A.
hasUrbanPopulationIn
Indicates that an entity has a specified urban population within a particular geographic area or administrative unit.
-
B.
hasUrbanAreaApprox
Indicates an approximate measure or estimate of the size or extent of an entity’s urban area.
-
C.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
D.
metropolitanAreaPopulationApproximate
chosen
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
-
E.
hasPeriUrbanPopulation
Indicates that an entity has a population living in peri-urban areas, i.e., zones at the transition between urban and rural regions.
- 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_69e24589d8348190b96422d13a678bc1 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f04b06481909004818ec8fc5a26 |
completed | April 29, 2026, 3:46 a.m. |
| PD | Predicate disambiguation | batch_69eed2d8c0608190afef4c4e530c0e2c |
completed | April 27, 2026, 3:07 a.m. |
Created at: April 17, 2026, 3:38 p.m.