Triple
T13921463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Differdange |
E334753
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Lobbes |
E1018280
|
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: Lobbes | Statement: [Differdange, hasTwinTown, Lobbes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lobbes Context triple: [Differdange, hasTwinTown, Lobbes]
-
A.
Lobbes
chosen
Lobbes is a historic municipality in the Walloon region of Belgium, known for its ancient abbey and picturesque rural setting.
-
B.
Lübars
Lübars is a historic, village-like district in Berlin’s Reinickendorf borough, known for its rural character, fields, and preserved traditional architecture within the city.
-
C.
Beaufays
Beaufays is a village in the municipality of Chaudfontaine in the province of Liège, Belgium.
-
D.
Villeblevin
Villeblevin is a small commune in north-central France, best known as the place where writer Albert Camus died in a car accident.
-
E.
Löwenberg
Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2aa5c1f481908a9d8786872f08fe |
completed | April 14, 2026, 11:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce7c4a788190a1e7619a00ab0c2e |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 10:16 p.m.