Triple
T4414656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CTA 5000-series railcars |
E94934
|
entity |
| Predicate | fleetNumberingPattern |
P44071
|
FINISHED |
| Object | 5001 and up |
—
|
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: 5001 and up | Statement: [CTA 5000-series railcars, fleetNumberingPattern, 5001 and up]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fleetNumberingPattern Context triple: [CTA 5000-series railcars, fleetNumberingPattern, 5001 and up]
-
A.
fleetNumbers
chosen
Indicates that there is an association between an entity and one or more identifying numbers assigned to it as part of a fleet.
-
B.
tailNumberPrefix
Indicates that one entity’s tail number begins with the specified prefix represented by the other entity.
-
C.
planeNumber
Indicates that an entity is associated with a specific airplane identification number (such as a tail number or flight number).
-
D.
aircraftDesignationPrefix
Indicates the standardized prefix used in an aircraft’s designation that conveys its type, role, or function.
-
E.
aircraftRegistration
Indicates that an aircraft is assigned a specific official registration identifier or code.
- 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_69b34539638c8190abfea3eb29425210 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b354e940d48190b49cca6796d60de4 |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.