Triple
T25684942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2011 Census of India |
E644040
|
entity |
| Predicate | maleLiteracyRate |
P158990
|
FINISHED |
| Object | 82.14 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: 82.14 percent | Statement: [2011 Census of India, maleLiteracyRate, 82.14 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maleLiteracyRate Context triple: [2011 Census of India, maleLiteracyRate, 82.14 percent]
-
A.
literacyStatus
Indicates whether an entity possesses the ability to read and write, or its level of literacy.
-
B.
typeOfLiteracy
Indicates the specific kind or category of literacy (e.g., digital, financial, media) that characterizes an entity’s literacy skills or practices.
-
C.
hasGenderRatioMale
Indicates the proportion or percentage of male individuals within a given population or group.
-
D.
genderRatio
Indicates the proportional relationship between different genders within a given group or population.
-
E.
literacyRequirementAdministeredBy
Indicates that the administration or enforcement of a literacy requirement is carried out by a specified agent or authority.
- 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_69f5fb7c000c819094efdcbb23ebddae |
completed | May 2, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69f4807f8680819098a524158d049c63 |
completed | May 1, 2026, 10:29 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 21, 2026, 8:06 p.m.