Triple
T27813586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North American locomotive market |
E702599
|
entity |
| Predicate | includesCustomerType |
P82393
|
FINISHED |
| Object | freight railroads |
—
|
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: freight railroads | Statement: [North American locomotive market, includesCustomerType, freight railroads]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesCustomerType Context triple: [North American locomotive market, includesCustomerType, freight railroads]
-
A.
customerType
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
B.
issuerCustomerType
Indicates the type or category of customer associated with or defined by the issuer.
-
C.
underlyingCompanyCustomerType
Indicates the type or category of customer relationship that an underlying company has (e.g., retail, institutional, corporate).
-
D.
hasSecondaryCustomerType
Indicates that an entity is associated with an additional, non-primary customer classification or role.
-
E.
customerGroup
chosen
Indicates a relationship in which an entity belongs to, is classified under, or is associated with a particular group of customers.
- 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_69ef840a16748190926719ab96120bae |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69fe72dca2f08190beff17de3d2aada6 |
completed | May 8, 2026, 11:33 p.m. |
| PD | Predicate disambiguation | batch_69fe70bca8d08190b810e1e616ceac44 |
completed | May 8, 2026, 11:24 p.m. |
Created at: April 27, 2026, 5:44 p.m.