Triple
T13064785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gif-sur-Yvette |
E329291
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Ahrensburg |
E249427
|
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: Ahrensburg | Statement: [Gif-sur-Yvette, hasTwinTown, Ahrensburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ahrensburg Context triple: [Gif-sur-Yvette, hasTwinTown, Ahrensburg]
-
A.
Ahrensburg
chosen
Ahrensburg is a town in northern Germany’s Schleswig-Holstein state, known for its historic castle and proximity to Hamburg.
-
B.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
C.
Ehringshausen
Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
-
D.
Hammelburg
Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
-
E.
Albershausen
Albershausen is a small municipality in the German state of Baden-Württemberg, located in the Göppingen district in southern 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980e9bdfc81908eb90fb50597df64 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d3398b08190a0fc4b6044576e0a |
completed | May 3, 2026, 7:08 p.m. |
Created at: April 9, 2026, 8:59 p.m.