Triple
T4658184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cleves |
E102459
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Xanten |
E323116
|
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: Xanten | Statement: [Cleves, hasNearbyCity, Xanten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xanten Context triple: [Cleves, hasNearbyCity, Xanten]
-
A.
Xanten
chosen
Xanten is a historic town in western Germany known for its well-preserved Roman archaeological park and medieval architecture.
-
B.
Andernach
Andernach is a historic German town on the Rhine River in Rhineland-Palatinate, known for its medieval architecture and one of the world’s highest cold-water geysers.
-
C.
Neunkirchen
Neunkirchen is a town in southwestern Germany known as one of the major urban centers and former industrial hubs of the state of Saarland.
-
D.
Heiligenhaus
Heiligenhaus is a small town in North Rhine-Westphalia, western Germany, known for its manufacturing industry and location between Düsseldorf and Essen.
-
E.
Aachen
Aachen is a historic German city near the borders with Belgium and the Netherlands, renowned for its medieval cathedral, role as a coronation site for Holy Roman Emperors, and significance in both World Wars.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd63271a548190bd9662b69a45d9a5 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaf5a0988190b097ef71301aebbe |
completed | March 21, 2026, 1:57 a.m. |
Created at: March 20, 2026, 1:15 p.m.