Triple
T19966049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ragusa |
E479934
|
entity |
| Predicate | hasCityWallHeight |
P138053
|
FINISHED |
| Object | up to about 25 meters |
—
|
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: up to about 25 meters | Statement: [Ragusa, hasCityWallHeight, up to about 25 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCityWallHeight Context triple: [Ragusa, hasCityWallHeight, up to about 25 meters]
-
A.
hasCityWallFunction
Indicates that an entity serves the functional role of a city wall, such as providing defense, enclosure, or boundary protection for an urban area.
-
B.
hasCityWallThickness
Indicates that an entity (such as a city or fortification) is associated with a specific measurement of the thickness of its defensive walls.
-
C.
hasCityWallName
Indicates that a city wall is associated with a specific name or designation.
-
D.
hasCityWallsFrom
Indicates that a city’s defensive walls originate from, or were constructed starting at, a specified source location or structure.
-
E.
cityWallLength
Indicates the measured length or extent of a city's defensive wall.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc4f47c8190a721f5e488150d81 |
completed | April 20, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69e537f7e4848190b431a69ec3f1b609 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c42c688190a22f4d31ec692377 |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:54 p.m.