Triple
T17052994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dinkelland |
E413747
|
entity |
| Predicate | usesRegionalLanguage |
P10892
|
FINISHED |
| Object | Tweants |
E413753
|
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: Tweants | Statement: [Dinkelland, usesRegionalLanguage, Tweants]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tweants Context triple: [Dinkelland, usesRegionalLanguage, Tweants]
-
A.
Tweants
chosen
Tweants is a Low Saxon regional dialect spoken in the Twente region of the eastern Netherlands.
-
B.
Tullistes
Tullistes are the inhabitants of the French city of Tulle, located in the Corrèze department in central France.
-
C.
Twents
Twents is a Dutch Low Saxon dialect spoken in the Twente region of the eastern Netherlands.
-
D.
Tinejdad
Tinejdad is a small town in eastern Morocco known for its oasis setting and traditional Berber culture.
-
E.
Toinette
Toinette is the sharp-witted, outspoken maid in Molière’s comedy "Le Malade imaginaire," known for her clever schemes and satirical commentary on her hypochondriac master.
- 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.