Triple
T7999290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monal Restaurant |
E186204
|
entity |
| Predicate | typicalVisitTime |
P6833
|
FINISHED |
| Object | evening |
—
|
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: evening | Statement: [Monal Restaurant, typicalVisitTime, evening]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVisitTime Context triple: [Monal Restaurant, typicalVisitTime, evening]
-
A.
typicalTimes
chosen
Indicates the usual or characteristic times at which an event, activity, or condition typically occurs.
-
B.
typicalAppointment
Indicates that an appointment represents a standard, usual, or commonly occurring scheduling arrangement between entities.
-
C.
popularTimeToVisit
Indicates the time period during which a place is most frequently visited or experiences peak visitor activity.
-
D.
typicalSchedule
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
-
E.
peakHours
Indicates that an action, event, or condition occurs during the busiest or most heavily trafficked time 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c9a12788190a5607a538f4e07c1 |
completed | March 31, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69cb0483d3b48190b250c7603d747bca |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:17 p.m.