Triple
T6178844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garden of France |
E137886
|
entity |
| Predicate | includesCity |
P3207
|
FINISHED |
| Object | Angers |
E71733
|
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: Angers | Statement: [Garden of France, includesCity, Angers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angers Context triple: [Garden of France, includesCity, Angers]
-
A.
Angers
chosen
Angers is a historic city in western France known for its medieval architecture, including the Château d'Angers and its famous Apocalypse Tapestry.
-
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.
Mâcon
Mâcon is a historic town in eastern France’s Burgundy region, known for its wine production and picturesque setting along the Saône River.
-
D.
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.
-
E.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
- 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dcb77948190b5385438f81bf0a8 |
completed | March 22, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7fa512324819092e3a0b449cdb9ba |
completed | March 28, 2026, 3:57 p.m. |
Created at: March 22, 2026, 4:18 p.m.