Triple
T8168458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namur Province |
E190753
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Doische
Doische is a rural municipality in Wallonia, Belgium, known for its agricultural landscape and proximity to the French border.
|
E715895
|
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: Doische | Statement: [Namur Province, contains, Doische]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doische Context triple: [Namur Province, contains, Doische]
-
A.
Dranske
Dranske is a small coastal municipality on the island of Rügen in Mecklenburg-Vorpommern, Germany, historically known for its strategic military and naval facilities.
-
B.
Dhiseig
Dhiseig is a small coastal settlement on the Isle of Mull in Scotland, known primarily as the usual starting point for ascents of the mountain Ben More.
-
C.
Dietl
Dietl is a German surname most notably associated with Eduard Dietl, a World War II German general.
-
D.
Kvasy
Kvasy is a village in western Ukraine’s Zakarpattia region, known as a starting point for hikes in the Carpathian Mountains and for its mineral springs.
-
E.
Disen
Disen is a residential neighborhood in Oslo, Norway, known for its mix of apartment blocks, green spaces, and convenient public transport connections.
- 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: Doische Triple: [Namur Province, contains, Doische]
Generated description
Doische is a rural municipality in Wallonia, Belgium, known for its agricultural landscape and proximity to the French border.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doische Target entity description: Doische is a rural municipality in Wallonia, Belgium, known for its agricultural landscape and proximity to the French border.
-
A.
Dranske
Dranske is a small coastal municipality on the island of Rügen in Mecklenburg-Vorpommern, Germany, historically known for its strategic military and naval facilities.
-
B.
Dhiseig
Dhiseig is a small coastal settlement on the Isle of Mull in Scotland, known primarily as the usual starting point for ascents of the mountain Ben More.
-
C.
Dietl
Dietl is a German surname most notably associated with Eduard Dietl, a World War II German general.
-
D.
Kvasy
Kvasy is a village in western Ukraine’s Zakarpattia region, known as a starting point for hikes in the Carpathian Mountains and for its mineral springs.
-
E.
Disen
Disen is a residential neighborhood in Oslo, Norway, known for its mix of apartment blocks, green spaces, and convenient public transport connections.
- 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_69ca82c0ef14819083713f4473dd847c |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb466abfe48190b4eb2f23b1e28668 |
completed | March 31, 2026, 3:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf4b68288190be7490119d46b242 |
completed | April 1, 2026, 6:46 a.m. |
| NEDg | Description generation | batch_69ccc311d4e8819080f4aeef8ee7dc3b |
completed | April 1, 2026, 7:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ccd83115fc8190a3e276bed0a00926 |
completed | April 1, 2026, 8:32 a.m. |
Created at: March 30, 2026, 5:39 p.m.