Triple
T3905804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lys |
E87201
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Armentières |
E229247
|
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: Armentières | Statement: [Lys, flowsThrough, Armentières]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Armentières Context triple: [Lys, flowsThrough, Armentières]
-
A.
Armentières
chosen
Armentières is a commune in northern France near the Belgian border, historically known for its textile industry and World War I significance.
-
B.
Beauvechain
Beauvechain is a municipality in Walloon Brabant, Belgium, known for its rural character and the presence of a major Belgian Air Component base.
-
C.
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.
-
D.
Péronne
Péronne is a historic town in northern France known for its role in World War I and its location in the Somme department.
-
E.
Ypres
Ypres is a historic town in western Belgium that was the site of several major and devastating battles during World War I.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeed1102b08190a9f5087ff9be0358 |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5337f3ae08190ba68fc20a3ba4692 |
completed | March 14, 2026, 10:07 a.m. |
Created at: March 9, 2026, 3:22 p.m.