Triple
T33149023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yanam region |
E848384
|
entity |
| Predicate | usesScriptForTelugu |
P150428
|
FINISHED |
| Object | Telugu script |
—
|
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: Telugu script | Statement: [Yanam region, usesScriptForTelugu, Telugu script]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesScriptForTelugu Context triple: [Yanam region, usesScriptForTelugu, Telugu script]
-
A.
hasNameInTeluguScript
Indicates that an entity is associated with a name written in the Telugu script.
-
B.
associatedLanguageScript
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
C.
usesScriptFor
chosen
Indicates that one entity employs or relies on a particular script or writing system for its representation, communication, or operation.
-
D.
languageOfScriptPromoted
Indicates that a particular language is associated with and promoted through the use of a given writing script.
-
E.
scriptUsedInIndia
Indicates that a particular writing system or script is used within the geographical or cultural context of India.
- 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_69f3495a458c8190a1d34b237ba0be3f |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd37b695c88190855801626f91c4cd |
completed | May 8, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69fd374cccf08190a230e87164af5938 |
completed | May 8, 2026, 1:07 a.m. |
Created at: May 1, 2026, 1:28 a.m.