Triple
T203144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derby, Derbyshire, England |
E4550
|
entity |
| Predicate | hasMetropolitanPopulation |
P1070
|
FINISHED |
| Object | approximately 490000 |
—
|
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: approximately 490000 | Statement: [Derby, Derbyshire, England, hasMetropolitanPopulation, approximately 490000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetropolitanPopulation Context triple: [Derby, Derbyshire, England, hasMetropolitanPopulation, approximately 490000]
-
A.
hasMetropolitan
Indicates that an entity is associated with, served by, or located within a specific metropolitan area.
-
B.
metropolitanAreaPopulationApproximate
chosen
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
-
C.
isMegacity
Indicates that a city has an extremely large population and urban area, typically qualifying it as a major global metropolitan center.
-
D.
hasMetropolitanAreaName
Indicates that an entity is associated with a metropolitan area identified by a specific name.
-
E.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f46b4f081909e5ee3718109a71f |
completed | Feb. 28, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69a25b4b42ec8190bef16bbbdd30a742 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.