Triple
T9560413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Forney locomotive |
E230656
|
entity |
| Predicate | hasCabPosition |
P89793
|
FINISHED |
| Object | toward rear above truck |
—
|
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: toward rear above truck | Statement: [Forney locomotive, hasCabPosition, toward rear above truck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabPosition Context triple: [Forney locomotive, hasCabPosition, toward rear above truck]
-
A.
hasCabType
Indicates that an entity is associated with or characterized by a specific type or category of cab.
-
B.
hasControlCab
Indicates that an entity is equipped with or includes a control cab used for operating or controlling it.
-
C.
hasPositionOn
Indicates that one entity occupies or holds a specific role, job, or spatial location relative to another entity.
-
D.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
E.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
- 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_69ca847e53a88190a60eed7e02257f10 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd994bde0c8190afcba5cb8fa8b984 |
completed | April 1, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69ccd594d0ac8190a81bc11a3a538167 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93e90048190a2b0d7c5c195ba98 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:03 p.m.