Triple
T7970360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chessy, France |
E185306
|
entity |
| Predicate | locatedOn |
P40
|
FINISHED |
| Object | Marne River |
E46315
|
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: Marne River | Statement: [Chessy, France, locatedOn, Marne River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marne River Context triple: [Chessy, France, locatedOn, Marne River]
-
A.
Marne
chosen
The Marne is a major river in northeastern France that flows through the Île-de-France region before joining the Seine near Paris.
-
B.
Marne
Marne is a small city located in Cass County in the southwestern part of the U.S. state of Iowa.
-
C.
Aube River
The Aube River is a major waterway in northeastern France that flows through the Champagne region before joining the Seine.
-
D.
Vallée de la Marne
Vallée de la Marne is a key subregion of France’s Champagne wine area, known for its vineyards along the Marne River and its significant production of Pinot Meunier–based sparkling wines.
-
E.
Aisne
Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
- 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_69ca8297699481909b75a405f01e03af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3bd304dc8190b9feee5e17fc66db |
completed | March 31, 2026, 3:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100a1ad688190b1a2fc91ce3dbdc3 |
completed | April 4, 2026, 12:14 p.m. |
Created at: March 30, 2026, 5:13 p.m.