Triple
T12968286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wissembourg |
E321324
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Landau in der Pfalz |
E707662
|
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: Landau in der Pfalz | Statement: [Wissembourg, nearbyCity, Landau in der Pfalz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Landau in der Pfalz Context triple: [Wissembourg, nearbyCity, Landau in der Pfalz]
-
A.
Landau in der Pfalz
chosen
Landau in der Pfalz is a historic university and wine-producing city in Germany’s Rhineland-Palatinate region, known as a key center of the Southern Palatinate wine route.
-
B.
Land of Hadeln
The Land of Hadeln is a historic coastal region in northern Germany, known for its fertile marshlands, dike systems, and long-standing agricultural traditions along the lower Elbe.
-
C.
Dinkelscherben
Dinkelscherben is a municipality in the Swabian region of Bavaria in southern Germany.
-
D.
Saal an der Saale
Saal an der Saale is a small municipality in northern Bavaria, Germany, situated along the Franconian Saale River.
-
E.
Bad Waldliesborn
Bad Waldliesborn is a spa village in the German region of Westphalia, known for its therapeutic mineral springs and health tourism.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e407e5081909424fc0c22483c28 |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8e4e1a48190b8f7253717746295 |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 9, 2026, 8:32 p.m.