Triple
T20930025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maratha region |
E515445
|
entity |
| Predicate | associatedWithLanguageScript |
P16462
|
FINISHED |
| Object | Devanagari |
—
|
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: Devanagari | Statement: [Maratha region, associatedWithLanguageScript, Devanagari]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithLanguageScript Context triple: [Maratha region, associatedWithLanguageScript, Devanagari]
-
A.
associatedLanguageScript
chosen
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
B.
languageOfScriptPromoted
Indicates that a particular language is associated with and promoted through the use of a given writing script.
-
C.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
-
D.
nativeNameScript
Indicates the writing system or script in which an entity’s native name is expressed.
-
E.
isMostWidelyUsedScriptByNumberOfLanguages
Indicates that a script is the one used by the largest number of distinct languages, compared to all other scripts.
- 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_69e0b4fb431c8190b9d40e6a72f0cc87 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f6545f8c81908a8c2f0e8d7b060e |
completed | April 21, 2026, 4 a.m. |
| PD | Predicate disambiguation | batch_69e5c9af1fe08190953366a466950140 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:49 p.m.