Triple
T11015137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franconville |
E260343
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Sankt Ingbert |
E327041
|
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: Sankt Ingbert | Statement: [Franconville, hasTwinTown, Sankt Ingbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sankt Ingbert Context triple: [Franconville, hasTwinTown, Sankt Ingbert]
-
A.
Sankt Ingbert
chosen
Sankt Ingbert is a town in the German state of Saarland, known historically for its coal and steel industries and its proximity to the French border.
-
B.
Odelzhausen
Odelzhausen is a municipality in Bavaria, Germany, known for its historic castle and location along the Autobahn between Munich and Augsburg.
-
C.
Lorenzkirch
Lorenzkirch is a small village in Saxony, Germany, known historically as the birthplace of Nobel Prize–winning physicist Wolfgang Paul.
-
D.
Landsberg am Lech
Landsberg am Lech is a historic Bavarian town in southern Germany known for its medieval old town, picturesque setting on the Lech River, and its association with the nearby Landsberg Prison.
-
E.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
- 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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797a558a08190bdb5779faa9adf05 |
completed | April 9, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e374d371ec8190aba9e77346c6e876 |
completed | April 18, 2026, 12:10 p.m. |
Created at: April 8, 2026, 9:25 p.m.