Triple
T98724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan NYP monoplane |
E1991
|
entity |
| Predicate | numberOfEngines |
P3542
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Ryan NYP monoplane, numberOfEngines, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEngines Context triple: [Ryan NYP monoplane, numberOfEngines, 1]
-
A.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
-
B.
typicalEngine
Indicates that an entity is the standard or commonly used engine for another entity (such as a vehicle, device, or system).
-
C.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
D.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
E.
numberBuilt
Indicates the total count of items or structures that have been constructed or produced.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24ff07d148190a59aee12c807659d |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24ebe7b1c8190a6bfbf31dc7c7f07 |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a24f4b4658819087902414959161fb |
completed | Feb. 28, 2026, 2:13 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.