Triple
T19516917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail Class 700 |
E488300
|
entity |
| Predicate | totalCarsBuilt |
P2091
|
FINISHED |
| Object | 1150 |
—
|
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: 1150 | Statement: [British Rail Class 700, totalCarsBuilt, 1150]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCarsBuilt Context triple: [British Rail Class 700, totalCarsBuilt, 1150]
-
A.
numberOfTrailerCarsBuilt
Indicates the total count of trailer cars that have been constructed.
-
B.
numberBuilt
chosen
Indicates the total count of items or structures that have been constructed or produced.
-
C.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
D.
totalProduction
Indicates the overall quantity of goods, services, or output produced by an entity or system over a specified period or within a defined scope.
-
E.
numberOfCarsPerUnit
Indicates the quantity of cars associated with each single unit of a specified measure (such as time, distance, or entity).
- 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359ca7648190804c4d655170fda3 |
completed | April 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7d0da88190aabf8b5799691fb1 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:40 p.m.