Triple
T2519286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union of Arras |
E55483
|
entity |
| Predicate | hasLocation |
P40
|
FINISHED |
| Object | Arras |
E187526
|
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: Arras | Statement: [Union of Arras, hasLocation, Arras]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arras Context triple: [Union of Arras, hasLocation, Arras]
-
A.
Arras
chosen
Arras is a historic city in northern France renowned for its Flemish-Baroque architecture, grand squares, and role as a strategic site in both World Wars.
-
B.
Saint-Omer
Saint-Omer is a historic town in northern France known for its medieval architecture, strategic military importance, and role in Franco-Spanish conflicts.
-
C.
Cambrai
Cambrai is a historic city in northern France known for its medieval heritage, role in World War I, and traditional confectionery.
-
D.
Valenciennes
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.
-
E.
Cambrésis
Cambrésis is a historic region in northern France centered around the city of Cambrai, known for its medieval bishopric and strategic importance in European conflicts.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd232ae188190b7b70806b466ee99 |
completed | March 7, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03104c21c81909fede29ce1e76d88 |
completed | March 10, 2026, 2:56 p.m. |
Created at: March 6, 2026, 9:46 p.m.