Triple
T4381565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McMenamins Edgefield |
E99140
|
entity |
| Predicate | hasNumberOfGuestRoomsApprox |
P2402
|
FINISHED |
| Object | over 100 guest 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 100 guest rooms | Statement: [McMenamins Edgefield, hasNumberOfGuestRoomsApprox, over 100 guest rooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfGuestRoomsApprox Context triple: [McMenamins Edgefield, hasNumberOfGuestRoomsApprox, over 100 guest rooms]
-
A.
approximateNumberOfRooms
Indicates an estimated or not precisely known count of rooms associated with an entity.
-
B.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
C.
numberOfHotelRooms
chosen
Indicates the total count of rooms that a given hotel has.
-
D.
numberOfGuestRoomWings
Indicates the count of distinct guest room wings associated with a given property or facility.
-
E.
bedCount
Indicates the number of beds associated with an entity, such as a room, facility, or accommodation.
- 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_69b3454ea8f48190a49c2436624d6ef6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352613dd481909e008a8db239a108 |
completed | March 12, 2026, 11:55 p.m. |
| PD | Predicate disambiguation | batch_69b34f557fe8819085032bf7f0cea5dc |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:18 p.m.