Triple
T19856622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Lorraine |
E477147
|
entity |
| Predicate | significantCity |
P12871
|
FINISHED |
| Object | Toul |
—
|
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: Toul | Statement: [Upper Lorraine, significantCity, Toul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toul Context triple: [Upper Lorraine, significantCity, Toul]
-
A.
Toul
chosen
Toul is a historic commune in northeastern France known for its medieval fortifications and impressive Gothic cathedral.
-
B.
Belfort
Belfort is the surname of Jordan Belfort, the American former stockbroker, motivational speaker, and author whose high-profile fraud case inspired the film "The Wolf of Wall Street."
-
C.
Dijon
Dijon is a historic city in eastern France renowned for its rich architectural heritage, former status as the capital of the Duchy of Burgundy, and its famous mustard.
-
D.
Nevers
Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
-
E.
Lillebonne
Lillebonne is a historic town in northern France’s Normandy region, known for its Roman archaeological remains and medieval heritage.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6586c14fc81908d34785f1088b0a9 |
completed | April 20, 2026, 4:46 p.m. |
Created at: April 10, 2026, 1:51 p.m.