Triple
T1258960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knoxville Convention Center |
E12454
|
entity |
| Predicate | meetingRoomCount |
P15338
|
FINISHED |
| Object | more than 20 meeting 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: more than 20 meeting rooms | Statement: [Knoxville Convention Center, meetingRoomCount, more than 20 meeting rooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetingRoomCount Context triple: [Knoxville Convention Center, meetingRoomCount, more than 20 meeting rooms]
-
A.
numberOfHotelRooms
Indicates the total count of rooms that a given hotel has.
-
B.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
-
C.
hasConferenceSpace
chosen
Indicates that an entity provides or includes dedicated space suitable for holding conferences, meetings, or similar gatherings.
-
D.
meetingNumber
Indicates the specific numerical identifier assigned to distinguish one meeting from others.
-
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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bfc3a2848190891e73b351019d5b |
completed | March 1, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6eefbc81908dddd7d2ef368186 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:50 p.m.