Triple
T8641645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maarkedal |
E204664
|
entity |
| Predicate | hasSubMunicipality |
P747
|
FINISHED |
| Object | Nukerke |
E747375
|
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: Nukerke | Statement: [Maarkedal, hasSubMunicipality, Nukerke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nukerke Context triple: [Maarkedal, hasSubMunicipality, Nukerke]
-
A.
Nukerke
chosen
Nukerke is a former village and municipality in East Flanders, Belgium, that was incorporated into the municipality of Maarkedal.
-
B.
Krakhuna
Krakhuna is a Georgian white grape variety from the Imereti region, known for producing aromatic, full-bodied wines with pronounced acidity.
-
C.
Nuka
Nuka is a scruffy, resentful lion and one of the antagonists in Disney’s The Lion King II: Simba’s Pride, known as Zira’s eldest son and Kovu’s jealous older brother.
-
D.
Naju
Naju is a historic city in South Korea known for its pear cultivation and location in the southwestern province of South Jeolla.
-
E.
Horki
Horki is a town in eastern Belarus known for its agricultural academy and regional administrative significance.
- 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4795b07081908bfc9ebf35a50f07 |
completed | March 31, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ceccb10f0881908db334cd090d3231 |
completed | April 2, 2026, 8:08 p.m. |
Created at: March 30, 2026, 6:28 p.m.