Triple
T6948617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canadian National Railway |
E160862
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | CN |
E160862
|
NE 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: CN | Statement: [Canadian National Railway, hasAbbreviation, CN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CN Context triple: [Canadian National Railway, hasAbbreviation, CN]
-
A.
CN
chosen
CN is a major Canadian freight railway company that operates an extensive rail network across Canada and into the United States.
-
B.
CN
CN is the commonly used abbreviation for Monaco’s National Council, the unicameral legislative body of the Principality.
-
C.
CN
CN is the vehicle registration code used for County Cavan in Ireland.
-
D.
Simplified Chinese
Simplified Chinese is a standardized form of written Chinese that uses characters with reduced strokes, primarily employed in mainland China and Singapore.
-
E.
Zhong Wen
Zhong Wen is the tough, determined police officer portrayed by Jackie Chan in the action film "Police Story 2013."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68850419081909fb426b8f5a304c7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6daad028c8190a6db5b2d029d4dfc |
completed | March 27, 2026, 7:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7618876948190abda9cb234bbe225 |
completed | March 28, 2026, 5:05 a.m. |
Created at: March 27, 2026, 2:28 p.m.