Triple
T596469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie on the MTA |
E17393
|
entity |
| Predicate | hasImagery |
P2386
|
FINISHED |
| Object | endless ride through subway stations |
—
|
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: endless ride through subway stations | Statement: [Charlie on the MTA, hasImagery, endless ride through subway stations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImagery Context triple: [Charlie on the MTA, hasImagery, endless ride through subway stations]
-
A.
hasScenicViewOf
Indicates that one entity offers a visually appealing or picturesque view of another entity.
-
B.
mapCoverage
Indicates the extent or area that is represented, covered, or included by a particular map.
-
C.
hasMountainScenery
Indicates that a place or area features views or landscapes dominated by mountains.
-
D.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
E.
hasIllustrations
chosen
Indicates that an entity includes or is accompanied by visual illustrations.
- F. None of above.
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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bd3e5e08190be95cb2009aad42d |
completed | March 1, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69a494cfb52c81908d816a4c5579c321 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.