Triple
T3835954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2 World Trade Center |
E91131
|
entity |
| Predicate | plannedElevatorCount |
P1274
|
FINISHED |
| Object | multiple high-speed elevators |
—
|
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: multiple high-speed elevators | Statement: [2 World Trade Center, plannedElevatorCount, multiple high-speed elevators]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plannedElevatorCount Context triple: [2 World Trade Center, plannedElevatorCount, multiple high-speed elevators]
-
A.
numberOfElevators
chosen
Indicates the total count of elevators associated with a given entity or location.
-
B.
hasNumberOfElevatorBanks
Indicates the relationship specifying how many distinct elevator banks are present in or associated with a given entity.
-
C.
hasElevators
Indicates that one entity is equipped with or contains one or more elevators for vertical transportation.
-
D.
hasEscalators
Indicates that one entity is equipped with or contains escalators that can be used for movement between different levels or areas.
-
E.
elevatorTopSpeed_m_per_s
Indicates the maximum speed, in meters per second, that an elevator can travel.
- 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb9a27508190b05e5312cc7c8033 |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74dcecc819098285483ec721b40 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:18 p.m.