Triple
T25684948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2011 Census of India |
E644040
|
entity |
| Predicate | stateWithHighestLiteracyRate |
P168543
|
FINISHED |
| Object | Kerala |
—
|
NE NERFINISHED |
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: Kerala | Statement: [2011 Census of India, stateWithHighestLiteracyRate, Kerala]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stateWithHighestLiteracyRate Context triple: [2011 Census of India, stateWithHighestLiteracyRate, Kerala]
-
A.
mostPopulousUnionTerritory
Indicates that the subject is the union territory with the highest population among all union territories in the relevant context.
-
B.
literacyRateOverall
Indicates the proportion of a population that can read and write at a specified minimum level of proficiency.
-
C.
femaleLiteracyRate
Indicates the proportion of females in a population who can read and write at a specified minimum level of proficiency.
-
D.
rankByPopulationInIndia
Indicates the relative ordering of entities based on their population size within India.
-
E.
leastPopulousUnionTerritory
Indicates that the subject is the union territory with the smallest population among all union territories in the relevant context.
- F. None of above. chosen
Provenance (4 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_69f67595fa7c8190b6e9f7a8c700dd97 |
completed | May 2, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69f673c2f81c8190bf369226306eef09 |
completed | May 2, 2026, 9:59 p.m. |
| PDg | Predicate description generation | batch_69f674df80b08190adb7f7531083bbb1 |
completed | May 2, 2026, 10:04 p.m. |
Created at: April 21, 2026, 8:06 p.m.