Triple
T27883727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rune |
E705165
|
entity |
| Predicate | writingSystemAssociation |
P9330
|
FINISHED |
| Object | runic alphabet |
—
|
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: runic alphabet | Statement: [Rune, writingSystemAssociation, runic alphabet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: writingSystemAssociation Context triple: [Rune, writingSystemAssociation, runic alphabet]
-
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.
writingSystemAssociatedViaSaint
Indicates that a writing system is linked to a saint through that saint’s traditional, historical, or legendary role in its creation, promotion, or use.
-
C.
writingSystemUsedIn
Indicates that a particular writing system is employed for written communication within a given language, region, or context.
-
D.
writingSystemClass
chosen
Indicates that one entity is classified as a type or category of writing system to which the other entity belongs.
-
E.
writingSystemDevelopedFor
Indicates that a particular writing system was created or adapted specifically to be used for a given language, community, or purpose.
- 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_69ef96b39c448190a9b3aa6672a5168f |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f65876c52c8190bc889c7a67bd07f3 |
completed | May 2, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69f6575d89788190aca478e4aea05a65 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 27, 2026, 6:31 p.m.