Triple
T12814366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | URCA |
E306352
|
entity |
| Predicate | locatedInDepartment |
P40
|
FINISHED |
| Object | Marne |
E992253
|
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 | Statement: [URCA, locatedInDepartment, Marne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marne Context triple: [URCA, locatedInDepartment, Marne]
-
A.
Marne
chosen
Marne is a department in northeastern France known for its Champagne-producing vineyards and historic towns such as Reims and Châlons-en-Champagne.
-
B.
Marne
The Marne is a major river in northeastern France that flows through the Île-de-France region before joining the Seine near Paris.
-
C.
Marne
Marne is a small city located in Cass County in the southwestern part of the U.S. state of Iowa.
-
D.
Aisne
Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
-
E.
Aisne
Aisne is a river in northeastern France that flows through the Champagne and Picardy regions before joining the Oise River.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9beb30819097c256a5aab9a4c8 |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19226eb881908f76134a04e72548 |
completed | May 7, 2026, 10:58 p.m. |
Created at: April 9, 2026, 5:31 p.m.