Triple
T12007102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CP Regional |
E285807
|
entity |
| Predicate | fareLevelRelativeToIntercity |
P102635
|
FINISHED |
| Object | generally cheaper |
—
|
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: generally cheaper | Statement: [CP Regional, fareLevelRelativeToIntercity, generally cheaper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareLevelRelativeToIntercity Context triple: [CP Regional, fareLevelRelativeToIntercity, generally cheaper]
-
A.
fareLevelComparedToZone1
Indicates how the fare level for a given location or zone compares relative to the fare level in Zone 1.
-
B.
fareLevelRelativeToZone6
Indicates how the fare level for a given trip or location compares to the standard fare level defined for zone 6.
-
C.
comfortLevelComparedToConventionalTrains
Indicates how the comfort level of something compares relative to that of conventional trains.
-
D.
relativeSpeedComparedToConventionalTrains
Indicates how the speed of something compares to that of conventional trains, typically expressing whether it is faster, slower, or similar.
-
E.
comfortLevelRelativeToEconomy
Indicates the degree of comfort or quality relative to a standard economic or budget level.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c5cfc08190821e4b2940c51416 |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d9038e39f881908c58c19802ba2eb0 |
completed | April 10, 2026, 2:05 p.m. |
Created at: April 8, 2026, 9:46 p.m.