Triple
T20045404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amsterdam Bijlmer ArenA railway station |
E497544
|
entity |
| Predicate | operatedBy |
P86
|
FINISHED |
| Object | EBS |
—
|
NE NERFINISHED |
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: EBS | Statement: [Amsterdam Bijlmer ArenA railway station, operatedBy, EBS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EBS Context triple: [Amsterdam Bijlmer ArenA railway station, operatedBy, EBS]
-
A.
EBS
chosen
EBS is a Dutch public transport company that operates bus services in and around Amsterdam and other regions in the Netherlands.
-
B.
EBS
EBS is a German vehicle registration code assigned to the Bayreuth district in the state of Bavaria.
-
C.
Amazon EBS
Amazon EBS is a scalable, high-performance block storage service used with Amazon EC2 instances for persistent data storage in the AWS cloud.
-
D.
Amazon EFS
Amazon EFS is a fully managed, scalable, cloud-native file storage service that provides shared, elastic file systems for use with AWS compute resources.
-
E.
ECS
ECS is Amazon's fully managed container orchestration service that runs and scales Docker containers on AWS infrastructure.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662eea09481908d1001165e9d719c |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:37 p.m.