Triple
T3756085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walt Disney World bus transportation |
E82049
|
entity |
| Predicate | typicalServiceHours |
P3309
|
FINISHED |
| Object | early morning to late night |
—
|
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: early morning to late night | Statement: [Walt Disney World bus transportation, typicalServiceHours, early morning to late night]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalServiceHours Context triple: [Walt Disney World bus transportation, typicalServiceHours, early morning to late night]
-
A.
hoursOfOperation
Indicates the time periods during which a place or service is open and available for use.
-
B.
typicalBusinessHourStartLocalTime
Indicates the local time at which a business’s normal operating hours typically begin.
-
C.
openingHoursCharacteristic
Indicates a relationship that specifies particular attributes or features of an entity’s opening hours, such as patterns, exceptions, or special conditions.
-
D.
openingTime
Indicates the time at which a place, service, or event begins operating or becomes accessible.
-
E.
typicalSchedule
chosen
Indicates the usual or standard timing and sequence of activities or events 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb96dd908190b787b112ecd519df |
completed | March 8, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69adc04c851c8190ae5eaebf36df539b |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:35 p.m.