Triple
T11550217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brussels railway stations |
E273869
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Haren |
E817280
|
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: Haren | Statement: [Brussels railway stations, hasStation, Haren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haren Context triple: [Brussels railway stations, hasStation, Haren]
-
A.
Haren
chosen
Haren is a district in the northern part of Brussels, Belgium, known for its mix of residential areas, industrial zones, and transport infrastructure.
-
B.
Haren
Haren is a village in the Netherlands that functions as a submunicipality within the municipality of Oss in the province of North Brabant.
-
C.
Harku
Harku is a small settlement in northern Estonia located within Harku Parish, near the capital city of Tallinn.
-
D.
Hauran
Hauran is a historical region in southwestern Syria and northwestern Jordan, known for its fertile volcanic plains and ancient settlements.
-
E.
Hekari
Hekari is a regional dialect of the Kurmanji variety of the Kurdish language, spoken in parts of the Hakkari region.
- 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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d88a83f1e88190aabf11a4c8a6c9e5 |
completed | April 10, 2026, 5:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6e8396ed081909bdf381db3dacd62 |
completed | April 21, 2026, 3 a.m. |
Created at: April 8, 2026, 9:37 p.m.