Triple
T23407673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alpensia Ski Jumping Centre |
E559979
|
entity |
| Predicate | hasTemporarySeats |
P132734
|
FINISHED |
| Object | about 5000 |
—
|
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: about 5000 | Statement: [Alpensia Ski Jumping Centre, hasTemporarySeats, about 5000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTemporarySeats Context triple: [Alpensia Ski Jumping Centre, hasTemporarySeats, about 5000]
-
A.
hasTemporarySeatingCapacity
chosen
Indicates the number of seats that can be added or arranged temporarily for an entity, beyond its permanent seating capacity.
-
B.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
C.
hasTemporaryMembers
Indicates that an entity is associated with members whose involvement is limited to a non-permanent or time-bounded period.
-
D.
currentNumberOfSeats
Indicates the present total count of seats associated with an entity or context.
-
E.
hasTemporaryStation
Indicates that an entity is associated with a station or facility that is intended to be used only for a limited or non-permanent period.
- 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a50f3f90819084fb682597fee1e1 |
completed | April 29, 2026, 6:28 a.m. |
| PD | Predicate disambiguation | batch_69f061ed34288190a2e5e8cae03b0095 |
completed | April 28, 2026, 7:29 a.m. |
Created at: April 17, 2026, 5:38 p.m.