Triple
T12414539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viewliner |
E296601
|
entity |
| Predicate | hasSubclass |
P1244
|
FINISHED |
| Object | Viewliner I |
E296601
|
NE 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: Viewliner I | Statement: [Viewliner, hasSubclass, Viewliner I]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viewliner I Context triple: [Viewliner, hasSubclass, Viewliner I]
-
A.
Viewliner
chosen
Viewliner is a class of single-level passenger railcars used primarily by Amtrak for long-distance overnight service in the United States.
-
B.
Bondo
Bondo is a town in western Kenya’s Nyanza region, known as an administrative and commercial center near Lake Victoria.
-
C.
Uhu
Uhu was the nickname of the Heinkel He 219, a German World War II night fighter aircraft notable for its advanced radar and effectiveness against Allied bombers.
-
D.
Sharpie
Sharpie is a popular brand of permanent markers and writing instruments known for their bold, quick-drying ink used in homes, schools, and offices.
-
E.
Crayon
Crayon is a Nigerian singer and songwriter signed to Mavin Records, known for his Afropop and Afrobeat-influenced music.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6c4f6c8190bc99d3f7b64205c3 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6348ccaf88190aeb0dfb7fe1d8dec |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:55 p.m.