Triple
T7296336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadia district |
E164529
|
entity |
| Predicate | literacy |
P10080
|
FINISHED |
| Object | relatively high literacy rate compared to many Indian districts |
—
|
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: relatively high literacy rate compared to many Indian districts | Statement: [Nadia district, literacy, relatively high literacy rate compared to many Indian districts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literacy Context triple: [Nadia district, literacy, relatively high literacy rate compared to many Indian districts]
-
A.
literacyStatus
chosen
Indicates whether an entity possesses the ability to read and write, or its level of literacy.
-
B.
reading
Indicates that an entity is engaged in the activity of interpreting and understanding written or printed material from another entity or source.
-
C.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
-
D.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
E.
literaryInterest
Indicates that one entity has an interest in, appreciation of, or engagement with the literary works or writings of another entity.
- 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_69c6887a499881909dd23341399c59d8 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6eb8d0c6c8190b32cd08b9a5d96cc |
completed | March 27, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69c6e76e67d88190bd3ca6864f45845a |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3 p.m.