Triple
T7363661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Monument (Monas) |
E169812
|
entity |
| Predicate | elevatorCapacity |
P11680
|
FINISHED |
| Object | about 11 people |
—
|
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: about 11 people | Statement: [National Monument (Monas), elevatorCapacity, about 11 people]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: elevatorCapacity Context triple: [National Monument (Monas), elevatorCapacity, about 11 people]
-
A.
numberOfElevators
Indicates the total count of elevators associated with a given entity or location.
-
B.
elevatorTopSpeed_m_per_s
Indicates the maximum speed, in meters per second, that an elevator can travel.
-
C.
hasNumberOfElevatorBanks
Indicates the relationship specifying how many distinct elevator banks are present in or associated with a given entity.
-
D.
hasElevators
Indicates that one entity is equipped with or contains one or more elevators for vertical transportation.
-
E.
maximumPassengerCapacity
chosen
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
- 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_69c68a5ade988190885b7175f63b7534 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f26d6d6081909c7272a9ccae0d97 |
completed | March 27, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69c6f02d36108190bcb34a95e6a30bd7 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:06 p.m.