Triple
T32141279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vopiscus |
E820908
|
entity |
| Predicate | frequencyInEpigraphy |
P176052
|
FINISHED |
| Object | very low |
—
|
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: very low | Statement: [Vopiscus, frequencyInEpigraphy, very low]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyInEpigraphy Context triple: [Vopiscus, frequencyInEpigraphy, very low]
-
A.
frequencyInAntiquity
Indicates how often something occurred, appeared, or was used during ancient times.
-
B.
relevanceToEpigraphy
Indicates that something has significance, applicability, or usefulness specifically within the study and interpretation of inscriptions (epigraphy).
-
C.
pyramidTextInscriptions
Indicates that the subject has text inscriptions located on or within a pyramid.
-
D.
ancientLanguages
Indicates that the related entities are languages that originated in and were used during ancient historical periods.
-
E.
epigraphicType
Indicates the specific kind or category of an inscription as classified in epigraphic studies.
- 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_69f3490520d081909b2f1271dab75faa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6db6af1d88190989810182354d60f |
completed | May 3, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69f6d82d068c8190940a3200ed760e38 |
completed | May 3, 2026, 5:07 a.m. |
| PDg | Predicate description generation | batch_69f6db6a38d881909ecc75cc527910f2 |
completed | May 3, 2026, 5:21 a.m. |
Created at: May 1, 2026, 12:30 a.m.