Triple
T14096416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harsusi language |
E339263
|
entity |
| Predicate | primaryScriptUsage |
P6524
|
FINISHED |
| Object | mostly unwritten |
—
|
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: mostly unwritten | Statement: [Harsusi language, primaryScriptUsage, mostly unwritten]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryScriptUsage Context triple: [Harsusi language, primaryScriptUsage, mostly unwritten]
-
A.
primaryScript
chosen
Indicates the writing system or script that is chiefly used to represent the language or content of an entity.
-
B.
usedInScripts
Indicates that something (such as a tool, method, or resource) is employed or referenced within one or more scripts.
-
C.
hasScriptUsage
Indicates that one entity uses, employs, or is written in the script or writing system specified by another entity.
-
D.
scriptUsedCurrently
Indicates that a particular writing system or script is the one presently in use for a given language, text, or context.
-
E.
programUse
Indicates that one entity uses, employs, or makes use of a particular program or software application.
- 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5fb926288190a7f0f50d1d585d76 |
completed | April 14, 2026, 3:39 p.m. |
| PD | Predicate disambiguation | batch_69de05b2f7e481908a9a7d40153234c0 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:22 p.m.