Triple
T10612993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langar Hall |
E276050
|
entity |
| Predicate | mealSchedule |
P3309
|
FINISHED |
| Object | regularly served meals |
—
|
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: regularly served meals | Statement: [Langar Hall, mealSchedule, regularly served meals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mealSchedule Context triple: [Langar Hall, mealSchedule, regularly served meals]
-
A.
mealPeriod
Indicates the time-of-day category (such as breakfast, lunch, or dinner) during which a meal or food-related event occurs.
-
B.
typicalSchedule
chosen
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
-
C.
scheduleUS
Indicates that an entity arranges or assigns a time for something specifically within the context of the United States.
-
D.
middayService
Indicates a service or activity that takes place around the middle of the day.
-
E.
scheduleUsage
Indicates how and when a schedule is applied or utilized in relation to associated entities or events.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df5baf94819097b14a73af058f35 |
completed | April 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69d6dd7a223c8190854409d76368f3e8 |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 7:33 p.m.