Triple
T30320506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAN Lion’s City A47 |
E771184
|
entity |
| Predicate | hasChassisFamily |
P192061
|
FINISHED |
| Object | MAN Lion’s City chassis |
—
|
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: MAN Lion’s City chassis | Statement: [MAN Lion’s City A47, hasChassisFamily, MAN Lion’s City chassis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChassisFamily Context triple: [MAN Lion’s City A47, hasChassisFamily, MAN Lion’s City chassis]
-
A.
hasChassisType
Indicates that an entity is associated with or equipped with a specific type of chassis.
-
B.
supportsChassisType
Indicates that one entity is compatible with and can be used to support or accommodate a specified chassis type.
-
C.
chassisName
Indicates the designated name or identifier assigned to a chassis in a system or dataset.
-
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. 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_69f22489ee8481909344649bfbb92e83 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fcf36d2894819089b7db8e91b63c9d |
completed | May 7, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69fcf25c0a108190bfa823474098640b |
completed | May 7, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69fcf36bb86c8190a0a0ccf47cb56e5c |
completed | May 7, 2026, 8:17 p.m. |
Created at: April 29, 2026, 7:52 p.m.