Triple
T1451788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puplinge |
E31306
|
entity |
| Predicate | borderWith |
P224
|
FINISHED |
| Object | Thônex |
E31639
|
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: Thônex | Statement: [Puplinge, borderWith, Thônex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thônex Context triple: [Puplinge, borderWith, Thônex]
-
A.
Thônex
chosen
Thônex is a municipality in western Switzerland that forms part of the suburban area of Geneva near the French border.
-
B.
Melle
Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
-
C.
Issoire
Issoire is a historic town in central France’s Auvergne region, known for its Romanesque architecture and location in the valley of the Allier River.
-
D.
Laconnex
Laconnex is a small rural municipality in western Switzerland, located in the canton of Geneva near the French border.
-
E.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
- 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_69a499171a28819085b993a3ac78e363 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c57bc0908190a57e6bc3d20d5e3c |
completed | March 1, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad08c6f7c881908d1ef9f7897895a6 |
completed | March 8, 2026, 5:27 a.m. |
Created at: March 1, 2026, 8 p.m.