Triple
T14732592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iserlohn |
E346114
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Hagen |
E291575
|
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: Hagen | Statement: [Iserlohn, locatedNear, Hagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hagen Context triple: [Iserlohn, locatedNear, Hagen]
-
A.
Hagen
Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
-
B.
Hagen
Hagen is a formidable and cunning warrior in the medieval German epic "Nibelungenlied," best known for betraying and killing the hero Siegfried.
-
C.
Hagen
chosen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
D.
Hagen
Hagen is a small locality in northeastern France that forms part of the administrative area of the canton of Yutz in the Moselle department.
-
E.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec72ea9348190817efcdaa973d7f7 |
completed | April 14, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025e6bc748190b8099e7559569a90 |
completed | May 10, 2026, 6:29 a.m. |
Created at: April 10, 2026, 1:29 a.m.