Triple
T28379822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens SWT-3.6-120 |
E718859
|
entity |
| Predicate | hasRotorDiameterClass |
P98478
|
FINISHED |
| Object | 120-meter class |
—
|
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: 120-meter class | Statement: [Siemens SWT-3.6-120, hasRotorDiameterClass, 120-meter class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRotorDiameterClass Context triple: [Siemens SWT-3.6-120, hasRotorDiameterClass, 120-meter class]
-
A.
rotorDiameter
Indicates the relationship where a rotor is associated with a specific measurement representing the diameter of its circular span.
-
B.
rotorType
Indicates the specific kind or category of rotor associated with an entity.
-
C.
hasRimDiameter
Indicates that one entity has a rim whose diameter is measured by or corresponds to the value or object represented by the other entity.
-
D.
hasRoundWindowDiameter
Indicates that an entity possesses a round window whose size is specified by its diameter.
-
E.
hasDiameterClass
chosen
Indicates that an entity is associated with a specific category or range based on the size of its diameter.
- 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_69eff6ee5afc8190bd7375a29f0cc6c6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: April 28, 2026, 1:05 a.m.