Triple
T25684939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2011 Census of India |
E644040
|
entity |
| Predicate | urbanPopulationShare |
P158710
|
FINISHED |
| Object | 31.16 percent |
—
|
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: 31.16 percent | Statement: [2011 Census of India, urbanPopulationShare, 31.16 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanPopulationShare Context triple: [2011 Census of India, urbanPopulationShare, 31.16 percent]
-
A.
populationShareApproximate
chosen
Indicates that one entity’s share of a total population is approximately equal to a specified proportion or percentage, allowing for some margin of error.
-
B.
populationFocus
Indicates that something is primarily directed toward, concerned with, or designed for a particular population or demographic group.
-
C.
populationConcentration
Indicates the degree to which a population is densely gathered or distributed within a specific area or region.
-
D.
hasUrbanPopulationIn
Indicates that an entity has a specified urban population within a particular geographic area or administrative unit.
-
E.
permanentPopulation
Indicates that an entity has a stable, long-term resident population rather than a temporary or transient presence.
- 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_69e77e8046888190b07ffa58c7e2c37a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5fb7c000c819094efdcbb23ebddae |
completed | May 2, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69f4807f8680819098a524158d049c63 |
completed | May 1, 2026, 10:29 a.m. |
Created at: April 21, 2026, 8:06 p.m.