Triple
T15487037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dakhni Urdu masnavis |
E377072
|
entity |
| Predicate | roleInLiteraryHistory |
P15594
|
FINISHED |
| Object | earliest extended narrative use of Urdu |
—
|
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: earliest extended narrative use of Urdu | Statement: [Dakhni Urdu masnavis, roleInLiteraryHistory, earliest extended narrative use of Urdu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInLiteraryHistory Context triple: [Dakhni Urdu masnavis, roleInLiteraryHistory, earliest extended narrative use of Urdu]
-
A.
literaryRole
Indicates the specific narrative or functional role an entity holds within a literary work or text.
-
B.
nameInLiterature
Indicates that a particular name is used or appears for an entity within a literary work or context.
-
C.
literaryAuthor
Indicates that one entity is the author or writer of a literary work represented by the other entity.
-
D.
literaryInfluence
Indicates that one entity has had a significant impact on the style, themes, or development of another entity’s literary work.
-
E.
hasLiterarySignificance
chosen
Indicates that something holds notable importance, influence, or value within the realm of literature or literary studies.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f8f71a08190a440ff19dcc65312 |
completed | April 16, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69ded2874b788190999158e0f043be21 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:47 a.m.