Triple
T31077607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regionary Catalogues of Rome |
E792007
|
entity |
| Predicate | numberOfRegionsDescribed |
P2355
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [Regionary Catalogues of Rome, numberOfRegionsDescribed, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRegionsDescribed Context triple: [Regionary Catalogues of Rome, numberOfRegionsDescribed, 14]
-
A.
numberOfRegions
chosen
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
numberOfCitiesDescribed
Indicates the quantity of distinct cities that are mentioned or described in relation to a given subject or context.
-
C.
numberOfOrdinaryStatuteRegions
Indicates the count of regions governed by ordinary statutes associated with a given entity or context.
-
D.
numberOfNewRegions
Indicates the count of regions that have been newly created or added within a specified context or time frame.
-
E.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
- 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_69f224ccdbbc81909b0cdb4cc2d70c7a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00be8ee4bc8190b795d9606f0e490c |
completed | May 10, 2026, 5:21 p.m. |
| PD | Predicate disambiguation | batch_6a00bde163c88190867104bd08cac2ee |
completed | May 10, 2026, 5:18 p.m. |
Created at: April 29, 2026, 9:02 p.m.