Triple
T3112897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lao |
E64990
|
entity |
| Predicate | writingSystemExcludes |
P45401
|
FINISHED |
| Object | inherent 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: inherent spaces between words | Statement: [Lao, writingSystemExcludes, inherent spaces between words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: writingSystemExcludes Context triple: [Lao, writingSystemExcludes, inherent 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.
writingSystemUsedIn
Indicates that a particular writing system is employed for written communication within a given language, region, or context.
-
C.
writingSystemFeatures
Indicates the specific structural or functional characteristics that define how a particular writing system represents language.
-
D.
writingSystemScope
Indicates the range or extent of content, languages, or contexts to which a particular writing system applies or is used.
-
E.
writingSystemClass
Indicates that one entity is classified as a type or category of writing system to which the other entity belongs.
- 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_69ad857fcc088190b0c4d45a5cde6f61 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada43c79448190aa72f707319e8c5e |
completed | March 8, 2026, 4:30 p.m. |
| PD | Predicate disambiguation | batch_69ad9df25d4c81908ff0f6cff55d0563 |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada0f7c21c819087e9992f5fe30a37 |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 8, 2026, 3:04 p.m.