Triple
T19356496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midelt Province |
E484160
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Midelt |
—
|
NE NERFINISHED |
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: Midelt | Statement: [Midelt Province, hasSettlement, Midelt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Midelt Context triple: [Midelt Province, hasSettlement, Midelt]
-
A.
Midelt
chosen
Midelt is a town in central Morocco known as a regional center for apple production and as a gateway to the Middle Atlas and High Atlas mountains.
-
B.
Mitteltal
Mitteltal is a village and district within the Black Forest municipality of Baiersbronn in Baden-Württemberg, Germany, known for its scenic valley setting and tourism.
-
C.
Midilli
Midilli was the former German light cruiser SMS Breslau, serving in the Ottoman Navy during World War I and known for its operations in the Aegean and Black Seas alongside the battlecruiser Goeben.
-
D.
Euromed
Euromed is a high-speed long-distance train service in Spain that primarily connects major cities along the Mediterranean coast.
-
E.
Mitte
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e619096c1081909ce2cbf7ae804e73 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 10, 2026, 1:34 p.m.