Triple
T712176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens S70 |
E14233
|
entity |
| Predicate | carbodyMaterial |
P19785
|
FINISHED |
| Object | steel |
—
|
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: steel | Statement: [Siemens S70, carbodyMaterial, steel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carbodyMaterial Context triple: [Siemens S70, carbodyMaterial, steel]
-
A.
chassisMaterialFeature
Indicates that an entity has a chassis characterized by a specific material-related feature or property.
-
B.
chassis
Indicates that one entity serves as the structural frame or supporting base (chassis) for another entity.
-
C.
fuselageType
Indicates the specific structural or design category of an aircraft’s fuselage that an entity belongs to or uses.
-
D.
vehicleLayout
Indicates how the components or seating within a vehicle are arranged or configured relative to each other.
-
E.
rollingStockType
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
- 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a77fcc6881908a025bb21e44ad56 |
completed | March 1, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f221b081909fbaa689fb20eb3e |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a77e42e081909a6f2d1bfdc78ef0 |
completed | March 1, 2026, 8:54 p.m. |
Created at: March 1, 2026, 7:36 p.m.