Triple
T20757040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BSL |
E510875
|
entity |
| Predicate | servesRegion |
P82
|
FINISHED |
| Object | Alsace region |
E19573
|
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: Alsace region | Statement: [BSL, servesRegion, Alsace region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alsace region Context triple: [BSL, servesRegion, Alsace region]
-
A.
Alsace
Alsace is a station on the Lille Metro system in northern France, serving local urban transit passengers.
-
B.
Alsace
chosen
Alsace is a historical and cultural region in northeastern France known for its blend of French and German influences, picturesque villages, and renowned wines.
-
C.
Alsacia
Alsacia is a Madrid Metro station on Line 2 serving the San Blas-Canillejas district in eastern Madrid, Spain.
-
D.
Vosges region
The Vosges region is a mountainous area in northeastern France known for its forested peaks, scenic valleys, and role in early Renaissance humanist and cartographic activity.
-
E.
French Lorraine
French Lorraine is a historical region in northeastern France whose culture reflects a blend of French and Germanic influences.
- 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_69e0b4c909ec8190b05987f1639513f6 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c23113c88190a567c3a098cf7552 |
completed | April 21, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08e84a82308190b3c146c79549dba4 |
completed | May 16, 2026, 9:57 p.m. |
Created at: April 16, 2026, 12:35 p.m.