Triple
T6969878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telugu literature |
E161572
|
entity |
| Predicate | modernThemes |
P53053
|
FINISHED |
| Object | social reform |
—
|
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: social reform | Statement: [Telugu literature, modernThemes, social reform]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modernThemes Context triple: [Telugu literature, modernThemes, social reform]
-
A.
themeExamples
Indicates that the related entity serves as an example or illustration of the theme expressed by the subject.
-
B.
themeFor
Indicates that something serves as the central subject, topic, or focus for another thing (such as an event, work, or activity).
-
C.
modernFocus
chosen
Indicates a relationship where attention, emphasis, or primary relevance is directed toward contemporary or current aspects rather than historical or traditional ones.
-
D.
modernUse
Indicates how something is currently used or applied in modern times.
-
E.
modernAccess
Indicates that an entity has contemporary, up-to-date means or methods of accessing or interacting with another entity or resource.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db1649288190a52c7dab57b3c7dc |
completed | March 27, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.