Triple
T28296769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine of Aragon’s former apartments |
E713590
|
entity |
| Predicate | hasTypeOfOccupancy |
P191966
|
FINISHED |
| Object | final residence |
—
|
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: final residence | Statement: [Catherine of Aragon’s former apartments, hasTypeOfOccupancy, final residence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfOccupancy Context triple: [Catherine of Aragon’s former apartments, hasTypeOfOccupancy, final residence]
-
A.
hasOccupancy
Indicates that an entity is currently filled, used, or inhabited to a certain extent or by a certain number of occupants.
-
B.
hasOccupancyStatus
Indicates the current usage or availability state of something, such as whether it is occupied, vacant, or otherwise in use.
-
C.
allowedOccupationOf
Indicates that one entity is permitted or authorized to hold or perform the occupation associated with another entity.
-
D.
occupancyRequirement
Indicates that a condition specifies how many or which entities must be present in or using a particular space or resource.
-
E.
fieldOfOccupant
Indicates the specific professional or academic field in which an occupant is engaged or associated.
- 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_69efb524ab688190a1ce7ee7c9520932 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69fcf1b3d9a08190850b388308656266 |
completed | May 7, 2026, 8:10 p.m. |
| PD | Predicate disambiguation | batch_69fcf0226d8c8190b23dceafb1794995 |
completed | May 7, 2026, 8:03 p.m. |
| PDg | Predicate description generation | batch_69fcf1b241888190a243f07051c71383 |
completed | May 7, 2026, 8:10 p.m. |
Created at: April 27, 2026, 11:32 p.m.