Triple
T6252679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amud el-Sawari |
E140084
|
entity |
| Predicate | hasReliefOrInscription |
P16756
|
FINISHED |
| Object | Greek dedicatory inscription to Diocletian |
—
|
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: Greek dedicatory inscription to Diocletian | Statement: [Amud el-Sawari, hasReliefOrInscription, Greek dedicatory inscription to Diocletian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReliefOrInscription Context triple: [Amud el-Sawari, hasReliefOrInscription, Greek dedicatory inscription to Diocletian]
-
A.
hasNumberOfNamesInscribed
Indicates the quantity of distinct names that are inscribed on a given entity.
-
B.
materialTypicallyInscribedOn
Indicates the material that is most commonly used as the surface or medium on which something is inscribed.
-
C.
isInscribedOn
Indicates that text, symbols, or markings are written, carved, or otherwise permanently placed onto the surface of an object.
-
D.
hasGraveInscription
Indicates that an entity (typically a grave or tomb) bears a specific inscription engraved or written on it.
-
E.
hasInscriptions
chosen
Indicates that an object, surface, or artifact bears written, carved, or engraved inscriptions on it.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063417c8c8190945049881819d307 |
completed | March 22, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69c05605566c81908e197f5accd072d2 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:24 p.m.