Triple
T10706134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis III, Cardinal of Guise |
E252409
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | Saintes |
E630230
|
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: Saintes | Statement: [Louis III, Cardinal of Guise, deathPlace, Saintes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saintes Context triple: [Louis III, Cardinal of Guise, deathPlace, Saintes]
-
A.
Saintes
chosen
Saintes is a historic town in southwestern France, known for its well-preserved Roman and medieval heritage, including ancient monuments and religious sites.
-
B.
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.
-
C.
Bourges
Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
-
D.
Saumur
Saumur is a historic town in western France renowned for its château, wine production, and cavalry school on the banks of the Loire River.
-
E.
Niort
Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fddfbed48190810bb3faee473fde |
completed | April 9, 2026, 1:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f489b62b808190a3fc89e73e8ae2f7 |
completed | May 1, 2026, 11:08 a.m. |
Created at: April 8, 2026, 9:12 p.m.