Triple
T19340683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tremont House (Boston hotel) |
E483745
|
entity |
| Predicate | hasNumberOfBathingRooms |
P16001
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Tremont House (Boston hotel), hasNumberOfBathingRooms, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfBathingRooms Context triple: [Tremont House (Boston hotel), hasNumberOfBathingRooms, 8]
-
A.
numberOfBathrooms
chosen
Indicates the total count of bathrooms associated with an entity (such as a property or unit).
-
B.
hasBathhouse
Indicates that one entity possesses, contains, or is associated with a bathhouse facility.
-
C.
hasNumberOfMainRooms
Indicates the relationship that specifies how many main rooms are present in a given entity, such as a building or dwelling.
-
D.
typicalBathType
Indicates the usual or most common type of bath associated with an entity, such as a property, room, or accommodation.
-
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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61856c0948190a3166b3bf3810e43 |
completed | April 20, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:33 p.m.