Triple
T19665296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slow Train Coming |
E472183
|
entity |
| Predicate | followedBy |
P78
|
FINISHED |
| Object | Saved |
—
|
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: Saved | Statement: [Slow Train Coming, followedBy, Saved]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saved Context triple: [Slow Train Coming, followedBy, Saved]
-
A.
Saved
"Saved" is a television drama series featuring Elizabeth Reaser in a prominent role, centered on the intense personal and professional challenges within the world of emergency medical services.
-
B.
Saved
chosen
Saved is a controversial 1965 stage play by British dramatist Edward Bond, known for its stark portrayal of working-class life and its role in challenging theatre censorship in the UK.
-
C.
Save
The Save is a river in southwestern France that flows through the Occitanie region before joining the Garonne.
-
D.
SAVE
SAVE is the stock ticker symbol for Spirit Airlines, a U.S.-based ultra-low-cost carrier known for its no-frills service model.
-
E.
SAVE
SAVE is an Italian company, formally known as SAVE S.p.A., primarily involved in the management and development of airport infrastructure and related services.
- 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6416857c88190acb3adbf3e585fe5 |
completed | April 20, 2026, 3:08 p.m. |
Created at: April 10, 2026, 1:45 p.m.