Triple
T1731777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Connecticut Convention Center |
E37826
|
entity |
| Predicate | numberOfMeetingRooms |
P31827
|
FINISHED |
| Object | 25 |
—
|
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: 25 | Statement: [Connecticut Convention Center, numberOfMeetingRooms, 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMeetingRooms Context triple: [Connecticut Convention Center, numberOfMeetingRooms, 25]
-
A.
hasConferenceSpace
Indicates that an entity provides or includes dedicated space suitable for holding conferences, meetings, or similar gatherings.
-
B.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
-
C.
numberOfHotelRooms
Indicates the total count of rooms that a given hotel has.
-
D.
meetsDuring
Indicates that one entity encounters or comes together with another while a specified event or time interval is in progress.
-
E.
meetsAtLeast
Indicates that one entity encounters or comes into contact with another entity at least a specified minimum number of times or to a minimum required degree.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab5c553e508190b0f511b05e07fa20 |
completed | March 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69aa61c25a648190892de94c997fb983 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab5c54362881908895ab249cad8c1d |
completed | March 6, 2026, 10:59 p.m. |
Created at: March 4, 2026, 7:30 p.m.