Triple
T23265279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan Airlines First Class Lounge |
E588128
|
entity |
| Predicate | typicalAccessTime |
P6833
|
FINISHED |
| Object | before departure |
—
|
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: before departure | Statement: [Japan Airlines First Class Lounge, typicalAccessTime, before departure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAccessTime Context triple: [Japan Airlines First Class Lounge, typicalAccessTime, before departure]
-
A.
typicalAccessTimeFromImlil
Indicates the usual amount of time it takes to travel from Imlil to a given destination or point of interest.
-
B.
typicalTimes
chosen
Indicates the usual or characteristic times at which an event, activity, or condition typically occurs.
-
C.
hasTypicalUseTime
Indicates the usual or expected duration or time period during which something is commonly used or in operation.
-
D.
hasTypicalAccess
Indicates that one entity normally or customarily has the ability, permission, or means to access or use another entity.
-
E.
typicalDelay
Indicates the usual or expected amount of time by which something is delayed relative to its planned or nominal schedule.
- 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_69e25d148adc819088efbf42672604e9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f194cc3b908190aaefd036aa2b52b5 |
completed | April 29, 2026, 5:19 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:34 p.m.