Triple
T23479577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing 737-900ER |
E570366
|
entity |
| Predicate | modelVariantOf |
P104759
|
FINISHED |
| Object | Boeing 737-900 |
—
|
NE NERFINISHED |
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: Boeing 737-900 | Statement: [Boeing 737-900ER, modelVariantOf, Boeing 737-900]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelVariantOf Context triple: [Boeing 737-900ER, modelVariantOf, Boeing 737-900]
-
A.
exportVariantOf
Indicates that one entity is an exported version or externally released form derived from another, original entity.
-
B.
supportsModelVariant
Indicates that one entity is capable of operating with, being compatible with, or otherwise accommodating a specific variant of a model.
-
C.
brandNameVariant
Indicates that one brand name is an alternative or variant form of another brand name, such as a spelling, regional, or stylistic variation.
-
D.
likelyVariantOf
chosen
Indicates that one entity is probably a variant, version, or alternative form of another entity, based on available evidence or similarity.
-
E.
serviceVariantOf
Indicates that one service is a variant or alternative version of another, typically differing in configuration, features, or options while serving a similar core function.
- 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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a74f48d8819080e875aaea8b46b3 |
completed | April 29, 2026, 6:38 a.m. |
| PD | Predicate disambiguation | batch_69f0620ac3608190b36916261ea50f54 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 6:02 p.m.