Triple
T21745933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen Vincent Benét |
E536785
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | Western Star |
—
|
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: Western Star | Statement: [Stephen Vincent Benét, wrote, Western Star]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Western Star Context triple: [Stephen Vincent Benét, wrote, Western Star]
-
A.
Western Star
Western Star was a named passenger train of the Great Northern Railway that provided long-distance service across the northern United States.
-
B.
Western Star
chosen
"Western Star" is an epic, Pulitzer Prize–winning narrative poem by Stephen Vincent Benét that chronicles the westward expansion of the United States.
-
C.
Western Star
Western Star is a North American manufacturer of heavy-duty trucks known for its rugged, customizable vehicles used in vocational, off-road, and long-haul applications.
-
D.
Peterbilt
Peterbilt is an American manufacturer of premium heavy-duty and medium-duty trucks known for their durability, performance, and iconic styling.
-
E.
Kenworth
Kenworth is a prominent American manufacturer of heavy-duty and medium-duty trucks known for their durability and use in commercial freight and vocational applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a76540c8190b91a67f4a70869fb |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 16, 2026, 6:49 p.m.