Triple
T7198646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Botorrita inscriptions |
E168678
|
entity |
| Predicate | epigraphicType |
P75764
|
FINISHED |
| Object | long inscription |
—
|
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: long inscription | Statement: [Botorrita inscriptions, epigraphicType, long inscription]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: epigraphicType Context triple: [Botorrita inscriptions, epigraphicType, long inscription]
-
A.
materialTypicallyInscribedOn
Indicates the material that is most commonly used as the surface or medium on which something is inscribed.
-
B.
pyramidTextInscriptions
Indicates that the subject has text inscriptions located on or within a pyramid.
-
C.
scriptOfInscription
Indicates the writing system or script in which a given inscription is written.
-
D.
sculptorInscription
Indicates that an inscription identifies the sculptor responsible for creating a particular sculpture or artwork.
-
E.
iconographyType
Indicates the specific kind or category of visual symbolism or imagery used to represent something.
- 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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e92b8bc08190bfcdd34ce42e3448 |
completed | March 27, 2026, 8:31 p.m. |
| PD | Predicate disambiguation | batch_69c6e757fed4819091b0a096e3befc3a |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8fd9b848190b2b1beea5698422b |
completed | March 27, 2026, 8:30 p.m. |
Created at: March 27, 2026, 2:52 p.m.