Triple
T1072332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis Kahn |
E23357
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Saaremaa |
E24108
|
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: Saaremaa | Statement: [Louis Kahn, placeOfBirth, Saaremaa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saaremaa Context triple: [Louis Kahn, placeOfBirth, Saaremaa]
-
A.
Saaremaa
chosen
Saaremaa is the largest island of Estonia, known for its rugged coastline, medieval Kuressaare Castle, and distinctive windmills and juniper landscapes.
-
B.
Hiiumaa
Hiiumaa is Estonia’s second-largest island, located in the Baltic Sea and known for its unspoiled nature, lighthouses, and quiet rural landscapes.
-
C.
Valaam Islands
The Valaam Islands are a scenic archipelago in northwestern Russia best known for the historic Valaam Monastery and their rugged natural landscapes.
-
D.
Bornholm
Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
-
E.
Rügen
Rügen is Germany’s largest island, known for its chalk cliffs, seaside resorts, and beaches along the Baltic Sea coast.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b9296c5c8190a3060fbfdf24f029 |
completed | March 1, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4c1fb7948190b08c95e68d3de00e |
completed | March 7, 2026, 4:02 p.m. |
Created at: March 1, 2026, 7:42 p.m.