Triple
T824539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludwigslied |
E17824
|
entity |
| Predicate | manuscriptLocation |
P9488
|
FINISHED |
| Object | Valenciennes |
E112910
|
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: Valenciennes | Statement: [Ludwigslied, manuscriptLocation, Valenciennes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valenciennes Context triple: [Ludwigslied, manuscriptLocation, Valenciennes]
-
A.
Valenciennes
chosen
Valenciennes is a historic industrial city in northern France near the Belgian border, known for its former coal and steel industries and its rich artistic and architectural heritage.
-
B.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
C.
Troyes
Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
-
D.
Cambrai
Cambrai is a historic city in northern France known for its medieval heritage, role in World War I, and traditional confectionery.
-
E.
Lille
Lille is a historic industrial and cultural hub in northern France, known for its Flemish-influenced architecture, large student population, and role as a major European transport crossroads.
- 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ab7eb0a08190889463edb0e7bd59 |
completed | March 1, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac16f56bc0819094085d61f1f29f70 |
completed | March 7, 2026, 12:15 p.m. |
Created at: March 1, 2026, 7:38 p.m.