Triple
T15403922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aisne |
E368397
|
entity |
| Predicate | hasRightTributary |
P415
|
FINISHED |
| Object | Vesle |
E978455
|
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: Vesle | Statement: [Aisne, hasRightTributary, Vesle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vesle Context triple: [Aisne, hasRightTributary, Vesle]
-
A.
Vesle
chosen
The Vesle is a river in northeastern France that flows through the Champagne region and was a significant geographic feature during World War I battles.
-
B.
Vatne
Vatne is a village in Norway that forms part of the former municipality of Haram in Møre og Romsdal county.
-
C.
Fykseelva
Fykseelva is a river flowing through the municipality of Kvam in Vestland county, western Norway, known for its scenic valley and salmon fishing.
-
D.
Veavågen
Veavågen is a coastal village in Karmøy municipality in Rogaland county, Norway, known for its fishing industry and maritime character.
-
E.
Nidelva
Nidelva is the main river flowing through Trondheim, Norway, known for its scenic bends, historic waterfront buildings, and central role in the city’s landscape.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8fde64819082ec0c68df305561 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d40d3388190b1bd724238f928b1 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 3:19 a.m.