Triple
T37591515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monreale Cathedral |
E935269
|
entity |
| Predicate | numberOfCloisterColumns |
P112654
|
FINISHED |
| Object | over 200 paired columns |
—
|
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: over 200 paired columns | Statement: [Monreale Cathedral, numberOfCloisterColumns, over 200 paired columns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCloisterColumns Context triple: [Monreale Cathedral, numberOfCloisterColumns, over 200 paired columns]
-
A.
numberOfCloisters
Indicates the quantity of cloisters associated with a given entity.
-
B.
hasNumberOfCloisterCapitals
Indicates the specific count of cloister capitals associated with a given entity.
-
C.
numberOfColumnsInColonnade
chosen
Indicates the count of individual columns that make up a given colonnade.
-
D.
numberOfColonnades
Indicates the quantity of colonnades associated with a given entity or structure.
-
E.
numberOfEntrancePillars
Indicates the count of entrance pillars associated with or present at a given entity or location.
- 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_69f76ecf39c081909baffe597bb55273 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1553e08190bb7424c448cb1f33 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:18 p.m.