Triple
T1645472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin TV Tower |
E35571
|
entity |
| Predicate | restaurantRotationPeriod |
P29794
|
FINISHED |
| Object | 30 minutes |
—
|
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: 30 minutes | Statement: [Berlin TV Tower, restaurantRotationPeriod, 30 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: restaurantRotationPeriod Context triple: [Berlin TV Tower, restaurantRotationPeriod, 30 minutes]
-
A.
rotatesAmong
Indicates that an entity takes turns occupying or performing a role, position, or function in sequence with other entities.
-
B.
typicalHostRotation
Indicates that an entity normally or characteristically serves as the rotational host or primary environment in which another entity operates or resides.
-
C.
EncounterRestaurantClosure
Indicates that an entity experiences or comes across the situation of a restaurant being closed.
-
D.
EncounterRestaurantOpening
Indicates a situation where an entity comes across or experiences the opening or start of operations of a restaurant.
-
E.
traditionalVenuePeriod
Indicates the time span during which a venue is used in its customary or historically established manner.
- F. None of above. chosen
Provenance (4 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a919306fd48190a245fc95e0e759d9 |
completed | March 5, 2026, 5:48 a.m. |
| PD | Predicate disambiguation | batch_69a907cc9d348190b76b0d3f596e5a81 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a9192f975c8190bfd514a4b5a8786c |
completed | March 5, 2026, 5:48 a.m. |
Created at: March 4, 2026, 7:28 p.m.