Triple
T31136735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gumla district |
E793664
|
entity |
| Predicate | literacyRateComparedToNationalAverage |
P180349
|
FINISHED |
| Object | lower |
—
|
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: lower | Statement: [Gumla district, literacyRateComparedToNationalAverage, lower]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literacyRateComparedToNationalAverage Context triple: [Gumla district, literacyRateComparedToNationalAverage, lower]
-
A.
literacyRateOverall
Indicates the proportion of a population that can read and write at a specified minimum level of proficiency.
-
B.
femaleLiteracyRate
Indicates the proportion of females in a population who can read and write at a specified minimum level of proficiency.
-
C.
maleLiteracyRate
Indicates the percentage of males in a given population who can read and write at a specified minimum level of proficiency.
-
D.
literacyStatus
Indicates whether an entity possesses the ability to read and write, or its level of literacy.
-
E.
stateWithLowestLiteracyRate
Indicates the relationship where a particular state is identified as having the lowest literacy rate compared to all other states in the considered set.
- 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_69f224d2b3a48190aa9dd26fbf6eab1a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f73ae120bc8190bff94d38d7a7a00d |
completed | May 3, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69f73a38d0848190aa5139144b8561c6 |
completed | May 3, 2026, 12:06 p.m. |
| PDg | Predicate description generation | batch_69f73adfd9a081908adae6bd59dfefb9 |
completed | May 3, 2026, 12:09 p.m. |
Created at: April 29, 2026, 9:05 p.m.