Triple
T29422741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ATF Dingo 1 |
E746197
|
entity |
| Predicate | chassisBase |
P7999
|
FINISHED |
| Object |
Mercedes-Benz Unimog
The Mercedes-Benz Unimog is a highly versatile, all-terrain multipurpose truck renowned for its exceptional off-road capability and use in both civilian and military applications.
|
E1867777
|
NE FINISHED |
How this triple was built (3 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: Mercedes-Benz Unimog | Statement: [ATF Dingo 1, chassisBase, Mercedes-Benz Unimog]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mercedes-Benz Unimog Triple: [ATF Dingo 1, chassisBase, Mercedes-Benz Unimog]
Generated description
The Mercedes-Benz Unimog is a highly versatile, all-terrain multipurpose truck renowned for its exceptional off-road capability and use in both civilian and military applications.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chassisBase Context triple: [ATF Dingo 1, chassisBase, Mercedes-Benz Unimog]
-
A.
chassis
chosen
Indicates that one entity serves as the structural frame or supporting base (chassis) for another entity.
-
B.
chassisConstruction
Indicates how the chassis of an object is built or assembled, specifying the construction method or structural design used.
-
C.
chassisOrigin
Indicates the place or source from which a chassis was originally produced, manufactured, or derived.
-
D.
chassisFeature
Indicates that a particular feature, component, or characteristic is part of or associated with a chassis.
-
E.
chassisCode
Indicates the specific chassis designation or code assigned to a vehicle model to distinguish its underlying structural platform or variant.
- F. None of above.
Provenance (6 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_69f0a79f6d5c8190a350baed0157e06f |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f66a6aa3d08190887bfa4eed48faeb |
completed | May 2, 2026, 9:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a25d9285cd88190a9ed07e6719f8f3f |
completed | June 7, 2026, 8:48 p.m. |
| NEDg | Description generation | batch_6a25dd39b5e08190afdacb75ea8ef091 |
completed | June 7, 2026, 9:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a25e28ca1988190929154c0ceb6d42b |
completed | June 7, 2026, 9:28 p.m. |
| PD | Predicate disambiguation | batch_69f66339175c819080bd70f0ff7057b1 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 28, 2026, 3:06 p.m.