Triple
T1037541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madame de Montespan |
E22398
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object |
Bourges
Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
|
E206721
|
NE FINISHED |
How this triple was built (4 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: Bourges | Statement: [Madame de Montespan, placeOfDeath, Bourges]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bourges Context triple: [Madame de Montespan, placeOfDeath, Bourges]
-
A.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
-
B.
Mâcon
Mâcon is a historic town in eastern France’s Burgundy region, known for its wine production and picturesque setting along the Saône River.
-
C.
Poitiers
Poitiers is a historic city in western France known for its Romanesque architecture, medieval heritage, and role as a regional center in the Nouvelle-Aquitaine region.
-
D.
Troyes
Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
-
E.
Châteauroux
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bourges Triple: [Madame de Montespan, placeOfDeath, Bourges]
Generated description
Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bourges Target entity description: Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
-
A.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
-
B.
Mâcon
Mâcon is a historic town in eastern France’s Burgundy region, known for its wine production and picturesque setting along the Saône River.
-
C.
Poitiers
Poitiers is a historic city in western France known for its Romanesque architecture, medieval heritage, and role as a regional center in the Nouvelle-Aquitaine region.
-
D.
Troyes
Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
-
E.
Châteauroux
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
- F. None of above. chosen
Provenance (5 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b82b3ef08190bcd24845b4418d47 |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc977276881908405e2411eb14fa1 |
completed | March 8, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69adcdb1443881908d137644b6fcbdf1 |
completed | March 8, 2026, 7:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adce2773208190a076bb1cd4a2137f |
completed | March 8, 2026, 7:29 p.m. |
Created at: March 1, 2026, 7:41 p.m.