Triple
T17053263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tweants |
E413753
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Ootmarsums |
E1248505
|
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: Ootmarsums | Statement: [Tweants, hasDialect, Ootmarsums]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ootmarsums Context triple: [Tweants, hasDialect, Ootmarsums]
-
A.
Ootmarsum
chosen
Ootmarsum is a historic town in the Dutch province of Overijssel, known for its well-preserved medieval center, art galleries, and traditional cultural events.
-
B.
Borssum
Borssum is a district of the seaport city of Emden in Lower Saxony, Germany, known primarily as a residential area with local amenities.
-
C.
Oberems
Oberems is a village and municipal district within the municipality of Glashütten in the Hochtaunus region of Hesse, Germany.
-
D.
Harksheide
Harksheide was a former municipality in Schleswig-Holstein, Germany, that later became part of the city of Norderstedt.
-
E.
Hagenborgh
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
- 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_69d886cde3d481908d4d01ba88ba7eb7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3daa491008190ad013ee37532aa51 |
completed | April 18, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ed60d3481909c8144bcb01316a1 |
completed | May 11, 2026, 1:20 a.m. |
Created at: April 10, 2026, 5:34 a.m.