Triple
T867784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IN-DL |
E18743
|
entity |
| Predicate | hasSubdivisionTypeLabel |
P9832
|
FINISHED |
| Object | union territory |
—
|
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: union territory | Statement: [IN-DL, hasSubdivisionTypeLabel, union territory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubdivisionTypeLabel Context triple: [IN-DL, hasSubdivisionTypeLabel, union territory]
-
A.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
B.
representsSubdivisionOf
Indicates that one administrative or territorial unit is a smaller, constituent part of a larger administrative or territorial unit.
-
C.
hasSubdivisionCodeContext
Indicates that a subdivision code is interpreted within a specific coding or contextual framework that defines its meaning.
-
D.
countrySubdivisionType
chosen
Indicates the specific type or category of an administrative or territorial subdivision within a country (e.g., state, province, region).
-
E.
hasSubregionStatus
Indicates that one region holds an official or defined status as a subregion within a larger geographic or administrative area.
- 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_69a4938ce8688190a24bdfef82ba7d21 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac7e12b0819084d25b9a66888a91 |
completed | March 1, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69a4aa87504481909618a6815948da6f |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.