Triple
T13869123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palais des Congrès de Paris |
E333403
|
entity |
| Predicate | hasNumberOfMeetingRooms |
P31827
|
FINISHED |
| Object | around 80 |
—
|
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: around 80 | Statement: [Palais des Congrès de Paris, hasNumberOfMeetingRooms, around 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfMeetingRooms Context triple: [Palais des Congrès de Paris, hasNumberOfMeetingRooms, around 80]
-
A.
hasConferenceSpace
Indicates that an entity provides or includes dedicated space suitable for holding conferences, meetings, or similar gatherings.
-
B.
numberOfMeetingRooms
chosen
Indicates the total count of meeting rooms associated with a given entity or location.
-
C.
numberOfHotelRooms
Indicates the total count of rooms that a given hotel has.
-
D.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
-
E.
approximateNumberOfRooms
Indicates an estimated or not precisely known count of rooms associated with an entity.
- 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_69d81c5ced9c8190b0e9bcc6effe5959 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a101488190bd790b28033d38b9 |
completed | April 14, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69de05972f3881909977b4c843984f88 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:14 p.m.