Triple
T1422833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Pacific Cab Forward locomotive |
E30261
|
entity |
| Predicate | successorTractionType |
P28917
|
FINISHED |
| Object | diesel-electric locomotive |
—
|
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: diesel-electric locomotive | Statement: [Southern Pacific Cab Forward locomotive, successorTractionType, diesel-electric locomotive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: successorTractionType Context triple: [Southern Pacific Cab Forward locomotive, successorTractionType, diesel-electric locomotive]
-
A.
successorUse
Indicates that one entity is used or applied as the subsequent or follow-up use of another entity in a sequence or lifecycle.
-
B.
successorModel
Indicates that one model is the direct follow-up or replacement for another earlier model.
-
C.
successorCategory
Indicates that one category directly follows or replaces another in an ordered sequence or hierarchy.
-
D.
successor
Indicates that one entity directly follows another in an ordered sequence or position.
-
E.
successorSeries
Indicates that one series directly follows another in sequence, continuing or extending it as its successor.
- 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_69a498fb823c8190a67ce4c4837e641a |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c52e4ed881908d85e0cb9fe851ac |
completed | March 1, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69a4c4752abc8190a33b634c4d6fad28 |
completed | March 1, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69a4c52bbb748190aaa804438d31f4c2 |
completed | March 1, 2026, 11 p.m. |
Created at: March 1, 2026, 8 p.m.