Triple
T35479178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wanamaker Grand Court Organ |
E1025421
|
entity |
| Predicate | hasNumberOfPipes |
P3426
|
FINISHED |
| Object | over 28000 |
—
|
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: over 28000 | Statement: [Wanamaker Grand Court Organ, hasNumberOfPipes, over 28000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfPipes Context triple: [Wanamaker Grand Court Organ, hasNumberOfPipes, over 28000]
-
A.
hasPipes
Indicates that one entity is equipped with, contains, or is connected to one or more pipes.
-
B.
approximateNumberOfPipes
Indicates that the relationship specifies an estimated count of pipes associated with an entity.
-
C.
numberOfOrganPipes
chosen
Indicates the quantitative relationship specifying how many organ pipes are associated with a given organ or organ-related entity.
-
D.
hasPipeline
Indicates that one entity possesses, contains, or is associated with a pipeline used to transport or process something.
-
E.
hasPipelineConnection
Indicates that one entity is linked to another via a pipeline through which materials, fluids, or data can flow.
- 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_69f76dfadba0819083456aadcd6864ea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fef112398081909237c3872345968b |
completed | May 9, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69feefb14ec08190ab401987d8c84a23 |
completed | May 9, 2026, 8:26 a.m. |
Created at: May 3, 2026, 4:04 p.m.