Triple
T36563356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Harding |
E901902
|
entity |
| Predicate | hasTypicalWritingSystem |
P200730
|
FINISHED |
| Object | Latin alphabet |
—
|
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: Latin alphabet | Statement: [Robert Harding, hasTypicalWritingSystem, Latin alphabet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalWritingSystem Context triple: [Robert Harding, hasTypicalWritingSystem, Latin alphabet]
-
A.
hasOfficialWritingSystem
Indicates that an entity (typically a language) is associated with a formally recognized and standardized writing system used for official or standard purposes.
-
B.
hasCommonWritingSystem
Indicates that two entities use the same or mutually intelligible writing system for written communication.
-
C.
hasWritingSystemForMajorLanguage
Indicates that there exists a writing system used to represent a major language associated with the given entity.
-
D.
hasWritingSystemName
Indicates that a writing system is associated with a particular name or designation.
-
E.
isMostWidelyUsedWritingSystem
Indicates that the subject writing system is used by more people or in more contexts than any other writing system.
- 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_69f76e634e9481908c9ba1b87ab87c26 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffa9677be08190852c8ef6c2545fed |
completed | May 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69ffa6570e2c8190a9d7b37f12b91d9a |
completed | May 9, 2026, 9:25 p.m. |
| PDg | Predicate description generation | batch_69ffa96638108190942dcfbe0516c37a |
completed | May 9, 2026, 9:38 p.m. |
Created at: May 3, 2026, 4:11 p.m.