Triple
T4503998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allegro trains between Saint Petersburg and Helsinki |
E101287
|
entity |
| Predicate | numberOfTrainsets |
P23304
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Allegro trains between Saint Petersburg and Helsinki, numberOfTrainsets, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTrainsets Context triple: [Allegro trains between Saint Petersburg and Helsinki, numberOfTrainsets, 4]
-
A.
numberOfTrainsetsBuilt
Indicates the total count of trainsets that have been constructed or produced.
-
B.
vehiclesPerTrain
Indicates the number of vehicles that are attached to or make up a single train.
-
C.
trainCount
chosen
Indicates the number of trains associated with a given entity, context, or time period.
-
D.
numberOfRailwayTracks
Indicates the quantity of railway tracks associated with or present at a given entity or location.
-
E.
numberOfPowerCars
Indicates the relationship specifying how many power cars (self-propelled units) are associated with or contained in a given train or rail consist.
- 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_69bd43d175248190894dc58b5b395c26 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56fca7d4819081deb34628e04f00 |
completed | March 20, 2026, 2:17 p.m. |
| PD | Predicate disambiguation | batch_69bd521671688190bc655d25fa77eba2 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:01 p.m.