Triple
T7766069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwich Cathedral |
E176147
|
entity |
| Predicate | roofBossesCount |
P78225
|
FINISHED |
| Object | over 1000 carved and painted roof bosses |
—
|
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 1000 carved and painted roof bosses | Statement: [Norwich Cathedral, roofBossesCount, over 1000 carved and painted roof bosses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roofBossesCount Context triple: [Norwich Cathedral, roofBossesCount, over 1000 carved and painted roof bosses]
-
A.
roofCount
Indicates the number of distinct roofs associated with an entity (such as a building or structure).
-
B.
floorCount
Indicates the number of floors or levels that a building or structure has.
-
C.
roofLevel
Indicates the vertical level or story of a building at which the roof is located or begins.
-
D.
numberOfTowers
Indicates the quantity of towers associated with or contained by a given entity.
-
E.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
- 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_69c69962923c8190ac74d28b4f9fe0a0 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7043279748190b30882e9cc6cca54 |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c7016f4ce881909c2e9f610255187b |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c702a78edc819090c3448d33c8c381 |
completed | March 27, 2026, 10:20 p.m. |
Created at: March 27, 2026, 4:09 p.m.