Triple
T2343891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eure |
E45086
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Chartres |
E153197
|
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: Chartres | Statement: [Eure, flowsThroughCity, Chartres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chartres Context triple: [Eure, flowsThroughCity, Chartres]
-
A.
Chartres
chosen
Chartres is a historic city in northern France renowned for its well-preserved medieval old town and its UNESCO-listed Gothic cathedral, famed for its stained glass windows.
-
B.
Bourges
Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
-
C.
Lisieux
Lisieux is a town and commune in the Calvados department of Normandy in northwestern France, known as a major Catholic pilgrimage site associated with Saint Thérèse of Lisieux.
-
D.
Angoulême
Angoulême is a historic city in southwestern France known for its hilltop old town, medieval ramparts, and status as a major center of the French comics industry.
-
E.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6afe38c81909deb1de2a1c4eda9 |
completed | March 7, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae96253f0c81908b866c656c3f5bea |
completed | March 9, 2026, 9:43 a.m. |
Created at: March 4, 2026, 7:52 p.m.