Triple
T16020714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jurchen script |
E388588
|
entity |
| Predicate | hasInscriptionExample |
P1259
|
FINISHED |
| Object | Jurchen stele inscriptions |
—
|
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: Jurchen stele inscriptions | Statement: [Jurchen script, hasInscriptionExample, Jurchen stele inscriptions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInscriptionExample Context triple: [Jurchen script, hasInscriptionExample, Jurchen stele inscriptions]
-
A.
hasInscriptions
Indicates that an object, surface, or artifact bears written, carved, or engraved inscriptions on it.
-
B.
hasExample
chosen
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
C.
isInscribedOn
Indicates that text, symbols, or markings are written, carved, or otherwise permanently placed onto the surface of an object.
-
D.
hasTypicalInscriptionMethod
Indicates the usual or characteristic method by which inscriptions are applied or created on an object or surface.
-
E.
numberOfInscriptions
Indicates the total count of inscriptions associated with a given entity or object.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e1826a4f7c8190aba6d4f1075141b0 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:55 a.m.