Triple
T9718385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Brussels |
E235399
|
entity |
| Predicate | containsDistrict |
P22582
|
FINISHED |
| Object |
Haren
Haren is a district in the northern part of Brussels, Belgium, known for its mix of residential areas, industrial zones, and transport infrastructure.
|
E817280
|
NE FINISHED |
How this triple was built (4 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: [Northern Brussels, containsDistrict, Haren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haren Context triple: [Northern Brussels, containsDistrict, Haren]
-
A.
Haren
Haren is a village in the Netherlands that functions as a submunicipality within the municipality of Oss in the province of North Brabant.
-
B.
Harku
Harku is a small settlement in northern Estonia located within Harku Parish, near the capital city of Tallinn.
-
C.
Hauran
Hauran is a historical region in southwestern Syria and northwestern Jordan, known for its fertile volcanic plains and ancient settlements.
-
D.
Hekari
Hekari is a regional dialect of the Kurmanji variety of the Kurdish language, spoken in parts of the Hakkari region.
-
E.
Hareid
Hareid is a coastal village and municipality in western Norway known for its maritime industries and scenic fjord landscape.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Haren Triple: [Northern Brussels, containsDistrict, Haren]
Generated description
Haren is a district in the northern part of Brussels, Belgium, known for its mix of residential areas, industrial zones, and transport infrastructure.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Haren Target entity description: Haren is a district in the northern part of Brussels, Belgium, known for its mix of residential areas, industrial zones, and transport infrastructure.
-
A.
Haren
Haren is a village in the Netherlands that functions as a submunicipality within the municipality of Oss in the province of North Brabant.
-
B.
Harku
Harku is a small settlement in northern Estonia located within Harku Parish, near the capital city of Tallinn.
-
C.
Hauran
Hauran is a historical region in southwestern Syria and northwestern Jordan, known for its fertile volcanic plains and ancient settlements.
-
D.
Hekari
Hekari is a regional dialect of the Kurmanji variety of the Kurdish language, spoken in parts of the Hakkari region.
-
E.
Hareid
Hareid is a coastal village and municipality in western Norway known for its maritime industries and scenic fjord landscape.
- F. None of above. chosen
Provenance (5 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_69ca84d0123c819096f9dc3b6abb0881 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9e3ea61081908a5671fc5be9a738 |
completed | April 1, 2026, 10:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19f986f48819090376fb5aafb3da2 |
completed | April 4, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d1a3aaa5cc819086f560eded288070 |
completed | April 4, 2026, 11:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1a44ab2a48190a17d13906ab08129 |
completed | April 4, 2026, 11:52 p.m. |
Created at: March 30, 2026, 8:20 p.m.