Triple
T23937029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Elizabeth Hotel, Montreal |
E602666
|
entity |
| Predicate | roomNumberOfBedIn |
P154425
|
FINISHED |
| Object | Suite 1742 |
—
|
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: Suite 1742 | Statement: [Queen Elizabeth Hotel, Montreal, roomNumberOfBedIn, Suite 1742]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roomNumberOfBedIn Context triple: [Queen Elizabeth Hotel, Montreal, roomNumberOfBedIn, Suite 1742]
-
A.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
B.
bedCount
Indicates the number of beds associated with an entity, such as a room, facility, or accommodation.
-
C.
numberOfBathrooms
Indicates the total count of bathrooms associated with an entity (such as a property or unit).
-
D.
bedroomAssociatedWith
Indicates that there is a contextual or functional association between a bedroom and another entity (such as an object, feature, or space).
-
E.
numberOfHotelRooms
Indicates the total count of rooms that a given hotel 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_69e2953cf6e081909b8e25a10a52dddc |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1cfa1336c8190ac307a1b9497ba0b |
completed | April 29, 2026, 9:30 a.m. |
| PD | Predicate disambiguation | batch_69f1615518088190a206f54e2fdb14a3 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f16e348b548190b76e50f9b611f76d |
completed | April 29, 2026, 2:34 a.m. |
Created at: April 17, 2026, 9:05 p.m.