Triple
T1295790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacob Bekenstein |
E27649
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object | Helsinki |
E14163
|
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: Helsinki | Statement: [Jacob Bekenstein, placeOfDeath, Helsinki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helsinki Context triple: [Jacob Bekenstein, placeOfDeath, Helsinki]
-
A.
Helsinki
chosen
Helsinki is the capital and largest city of Finland, known for its coastal location on the Baltic Sea, modern design, and vibrant cultural life.
-
B.
Turku
Turku is one of Finland’s oldest and historically most important cities, located on the southwest coast and known for its medieval heritage and major Baltic Sea port.
-
C.
Tampere
Tampere is a major industrial and cultural city in southern Finland, historically significant as a key battleground in the Finnish Civil War.
-
D.
Tallinn
Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
-
E.
Lappeenranta
Lappeenranta is a city in southeastern Finland near the Russian border, known for its lakeside location on Saimaa and its role as a regional commercial and educational center.
- 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0f4031481908f5e3a53d8a72929 |
completed | March 1, 2026, 10:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbae7182081908b7045a15a2275d8 |
completed | March 7, 2026, 11:55 p.m. |
Created at: March 1, 2026, 7:51 p.m.