Triple
T26352392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Underground S8 Stock |
E662936
|
entity |
| Predicate | carCountPerTrain |
P42282
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [London Underground S8 Stock, carCountPerTrain, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carCountPerTrain Context triple: [London Underground S8 Stock, carCountPerTrain, 8]
-
A.
vehiclesPerTrain
chosen
Indicates the number of vehicles that are attached to or make up a single train.
-
B.
rowCountPerTrain
Indicates the number of rows associated with each individual train.
-
C.
trainsPerSide
Indicates the number of trains allocated or operating on each side (e.g., direction, platform, or segment) within a system or configuration.
-
D.
trainCount
Indicates the number of trains associated with a given entity, context, or time period.
-
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_69ee8130fc44819094e5ab1da201cd7b |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60fec2c3c8190b76c15fa8a97f0e6 |
completed | May 2, 2026, 2:53 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 10:46 p.m.