Triple
T17008839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winterthur Museum, Garden and Library |
E412643
|
entity |
| Predicate | numberOfPeriodRooms |
P29543
|
FINISHED |
| Object | over 175 period rooms |
—
|
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 175 period rooms | Statement: [Winterthur Museum, Garden and Library, numberOfPeriodRooms, over 175 period rooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeriodRooms Context triple: [Winterthur Museum, Garden and Library, numberOfPeriodRooms, over 175 period rooms]
-
A.
hasPeriodRooms
chosen
Indicates that an entity contains rooms that are decorated or preserved to reflect specific historical periods.
-
B.
numberOfHotelRooms
Indicates the total count of rooms that a given hotel has.
-
C.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
-
D.
numberOfEndowedRooms
Indicates the quantity of rooms that have been formally endowed or sponsored.
-
E.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d3853f548190910240a2145cc890 |
completed | April 18, 2026, 6:55 p.m. |
| PD | Predicate disambiguation | batch_69e35d552bc08190af17ef7659e094ef |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.