Triple
T637450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thai language |
E16655
|
entity |
| Predicate | writingSystemFeatures |
P18322
|
FINISHED |
| Object | no spaces between words |
—
|
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: no spaces between words | Statement: [Thai language, writingSystemFeatures, no spaces between words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: writingSystemFeatures Context triple: [Thai language, writingSystemFeatures, no spaces between words]
-
A.
writingSystem
Indicates that one entity is the script or system of written symbols used to represent the language or content of another entity.
-
B.
writingSystemClass
Indicates that one entity is classified as a type or category of writing system to which the other entity belongs.
-
C.
writingSystemScope
Indicates the range or extent of content, languages, or contexts to which a particular writing system applies or is used.
-
D.
writingSystemStatus
Indicates the current functional or sociolinguistic state of a writing system, such as whether it is actively used, obsolete, official, or endangered.
-
E.
writingSystemUsedSince
Indicates that a particular writing system has been in use starting from a specified point in time or period.
- 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_69a4936be1c88190af56540324b57da7 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a17b125481909a6ab53424954792 |
completed | March 1, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69a49d0629308190bcc137639567f7c2 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a4a1794a60819092b3dc3344426ed7 |
completed | March 1, 2026, 8:28 p.m. |
Created at: March 1, 2026, 7:35 p.m.