Triple
T7011467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Sumba Regency |
E162590
|
entity |
| Predicate | hasOfficialWritingSystem |
P73650
|
FINISHED |
| Object | Latin alphabet |
—
|
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: Latin alphabet | Statement: [Central Sumba Regency, hasOfficialWritingSystem, Latin alphabet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialWritingSystem Context triple: [Central Sumba Regency, hasOfficialWritingSystem, Latin alphabet]
-
A.
hasWritingSystemForMajorLanguage
Indicates that there exists a writing system used to represent a major language associated with the given entity.
-
B.
isMostWidelyUsedWritingSystem
Indicates that the subject writing system is used by more people or in more contexts than any other writing system.
-
C.
writingSystemUsedIn
Indicates that a particular writing system is employed for written communication within a given language, region, or context.
-
D.
hasOfficialOrthography
Indicates that an entity has a formally recognized and standardized system for writing its language or name.
-
E.
writingSystem
Indicates that one entity is the script or system of written symbols used to represent the language or content of another entity.
- 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_69c6885a127c8190867b059bdccf13ff |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc5729448190af66dbd6f3e8936e |
completed | March 27, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c790288190b7cbbaa4a5f9c91d |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8a4930081908f1ae1e6ca8a514c |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:34 p.m.