Triple
T27752651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andhra Pradesh–Karnataka border |
E701250
|
entity |
| Predicate | hasOfficialLanguageOnOneSide |
P167332
|
FINISHED |
| Object | Telugu |
—
|
NE NERFINISHED |
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 | Statement: [Andhra Pradesh–Karnataka border, hasOfficialLanguageOnOneSide, Telugu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialLanguageOnOneSide Context triple: [Andhra Pradesh–Karnataka border, hasOfficialLanguageOnOneSide, Telugu]
-
A.
hasLanguageOfficial
Indicates that a language holds official status within a given entity, such as a country, region, or organization.
-
B.
hasOfficialCountryLanguage
Indicates that a country recognizes a particular language as one of its official languages for governmental or legal purposes.
-
C.
haveDistinctOfficialLanguages
Indicates that the two entities each have their own official language and these official languages are not the same.
-
D.
hasNotableLanguageWithOfficialStatusIn
Indicates that a language holds an officially recognized and notable status within a specified jurisdiction or region.
-
E.
hasLanguageOfOfficialName
Indicates that an entity’s official name is expressed in a specified language.
- 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f66a6468ec8190a43ed6cd8c797f42 |
completed | May 2, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
| PDg | Predicate description generation | batch_69f6691da93081909deaf680614fc900 |
completed | May 2, 2026, 9:14 p.m. |
Created at: April 27, 2026, 4:21 p.m.