Triple
T700137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Picene |
E13980
|
entity |
| Predicate | corpusSize |
P425
|
FINISHED |
| Object | small corpus of 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: small corpus of inscriptions | Statement: [South Picene, corpusSize, small corpus of inscriptions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: corpusSize Context triple: [South Picene, corpusSize, small corpus of inscriptions]
-
A.
collectionSize
chosen
Indicates the total number of items contained within a specified collection.
-
B.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
C.
collaborationSize
Indicates the number of participants involved together in a given collaborative relationship or activity.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
E.
stateSize
Indicates the relative or absolute physical extent or dimensions of a state, such as its area or population size.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a544e3608190ac315c7aa9f88e7e |
completed | March 1, 2026, 8:44 p.m. |
| PD | Predicate disambiguation | batch_69a4a4ec8c748190b198492a0eea4445 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:36 p.m.