Triple
T443801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinhala |
E10172
|
entity |
| Predicate | hasScriptType |
P4427
|
FINISHED |
| Object | abugida |
—
|
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: abugida | Statement: [Sinhala, hasScriptType, abugida]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScriptType Context triple: [Sinhala, hasScriptType, abugida]
-
A.
containsScript
Indicates that one entity includes or embeds the script of another entity within it.
-
B.
scriptType
chosen
Indicates the classification or category of a script, specifying what kind of script it is (e.g., its format, purpose, or scripting language type).
-
C.
hasProjectType
Indicates that an entity is associated with, or classified under, a specific type or category of project.
-
D.
hasCurrentType
Indicates that an entity currently possesses or is classified under a specific type or category, as opposed to past or potential types.
-
E.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef43e8f88190a5d368add11a38c0 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2edde2b9c8190bd20b582eb4c5065 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.