Triple
T8241336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterloo |
E192540
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Lahnstein |
E214200
|
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: Lahnstein | Statement: [Waterloo, hasTwinTown, Lahnstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lahnstein Context triple: [Waterloo, hasTwinTown, Lahnstein]
-
A.
Lahnstein
chosen
Lahnstein is a historic town in western Germany, located on the Rhine River in the state of Rhineland-Palatinate.
-
B.
Waidberg
Waidberg is a wooded hill and recreational area on the outskirts of Zurich, Switzerland, known for its hiking trails, viewpoints, and proximity to the Hönggerberg.
-
C.
Marlenheim
Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
-
D.
Euerbach
Euerbach is a small municipality in the Schweinfurt district of northern Bavaria, Germany, known for its rural character and Franconian cultural heritage.
-
E.
Stühlingen
Stühlingen is a small town in the state of Baden-Württemberg in southwestern Germany, near the Swiss border, known for its scenic setting in the Black Forest region.
- 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_69ca82dc8f148190a2c75a98501a7b91 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb783e13648190abf34eb8c244ea17 |
completed | March 31, 2026, 7:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebb26dd048190bd7e4de4ae986b32 |
completed | April 2, 2026, 6:53 p.m. |
Created at: March 30, 2026, 5:47 p.m.