Triple
T18931719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobigny |
E463126
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Gross-Gerau |
E715048
|
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: Gross-Gerau | Statement: [Bobigny, twinnedWith, Gross-Gerau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gross-Gerau Context triple: [Bobigny, twinnedWith, Gross-Gerau]
-
A.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
B.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
-
C.
Gelnhausen
Gelnhausen is a historic town in the German state of Hesse, known for its well-preserved medieval architecture and former status as a Free Imperial City of the Holy Roman Empire.
-
D.
Groß-Gerau
chosen
Groß-Gerau is a town in the German state of Hesse that serves as the administrative seat of the Groß-Gerau district in the Rhine-Main region.
-
E.
Gerlachsheim
Gerlachsheim is a district of the town Lauda-Königshofen in the Main-Tauber-Kreis region of Baden-Württemberg, Germany.
- 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_69d8dcfdbbb881909964fa5a75bd0b48 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c9c0b84881909ad6da9522203df8 |
completed | April 20, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a071bc49a5c8190b25a20023a3e521d |
completed | May 15, 2026, 1:12 p.m. |
Created at: April 10, 2026, 11:59 a.m.