Triple
T3678860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patna |
E78060
|
entity |
| Predicate | populationUrbanAgglomeration |
P1070
|
FINISHED |
| Object | over 2 million |
—
|
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: over 2 million | Statement: [Patna, populationUrbanAgglomeration, over 2 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationUrbanAgglomeration Context triple: [Patna, populationUrbanAgglomeration, over 2 million]
-
A.
populationConcentration
Indicates the degree to which a population is densely gathered or distributed within a specific area or region.
-
B.
formsUrbanAreaWith
Indicates that two or more settlements are geographically and functionally connected so that together they constitute a single continuous urban area.
-
C.
largestUrbanConcentrationIn
Indicates that an entity represents the biggest or most populous urban area located within a specified geographic region.
-
D.
metropolitanAreaPopulationApproximate
chosen
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
-
E.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
- 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_69ad85e18c1c8190be8aafb227f39f48 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc46599188190a046eddb0d85c483 |
completed | March 8, 2026, 6:48 p.m. |
| PD | Predicate disambiguation | batch_69adb84be1fc81909721c871babb4633 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:25 p.m.