Triple
T25684937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2011 Census of India |
E644040
|
entity |
| Predicate | sexRatio |
P139448
|
FINISHED |
| Object | 943 females per 1000 males |
—
|
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: 943 females per 1000 males | Statement: [2011 Census of India, sexRatio, 943 females per 1000 males]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sexRatio Context triple: [2011 Census of India, sexRatio, 943 females per 1000 males]
-
A.
genderRatio
chosen
Indicates the proportional relationship between different genders within a given group or population.
-
B.
hasGenderRatioMale
Indicates the proportion or percentage of male individuals within a given population or group.
-
C.
hasGenderRatioFemale
Indicates the proportion or percentage of females relative to the total population in the described group or context.
-
D.
sexStatus
Indicates whether and how a sexual relationship or sexual activity exists or has occurred between the related entities.
-
E.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
- 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.