Triple
T15056919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leuven railway station |
E379519
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Hasselt |
E165235
|
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: Hasselt | Statement: [Leuven railway station, connectsTo, Hasselt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hasselt Context triple: [Leuven railway station, connectsTo, Hasselt]
-
A.
Hasselt
Hasselt is a historic small city in the Dutch province of Overijssel, known for its medieval center and canals.
-
B.
Hasselt
chosen
Hasselt is a city in northeastern Belgium that serves as the capital of the province of Limburg in the Flemish region.
-
C.
Vilvoorde
Vilvoorde is a city in the Flemish Region of Belgium, located just north of Brussels and known as part of the capital’s broader metropolitan area.
-
D.
Aalst
Aalst is a historic city in the Belgian province of East Flanders, known for its textile industry and famous annual carnival.
-
E.
Aarschot
Aarschot is a historic city in Belgium known for its medieval architecture, including a prominent Gothic church, and its location along the Demer River.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda937f788190899d81bbb2084443 |
completed | April 15, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff908410548190ada5d4f71d52919b |
completed | May 9, 2026, 7:52 p.m. |
Created at: April 10, 2026, 3:01 a.m.