Triple
T10388481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vladimir Voevodsky |
E244828
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object | Princeton |
E244565
|
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: Princeton | Statement: [Vladimir Voevodsky, placeOfDeath, Princeton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Princeton Context triple: [Vladimir Voevodsky, placeOfDeath, Princeton]
-
A.
Princeton
chosen
Princeton is a historic New Jersey town best known as the site of the pivotal 1777 Battle of Princeton during the American Revolutionary War and as home to Princeton University.
-
B.
Princeton
Princeton is a small city in southern West Virginia known as a regional hub for commerce, education, and access to outdoor recreation in the Appalachian region.
-
C.
Princeton
Princeton is a small town in Johnston County, North Carolina, known for its rural character and tight-knit community.
-
D.
Princeton
Princeton is a small but rapidly growing city in Collin County, Texas, situated in the northeastern part of the Dallas–Fort Worth metropolitan area.
-
E.
Princeton
Princeton is a small city in central Wisconsin known for its historic downtown, outdoor recreation along the Fox River, and popular weekly flea market.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9a59d688190b1da1ea0ed48fafa |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dbf4685881908cc2ade858b673aa |
completed | April 10, 2026, 11:16 a.m. |
Created at: April 6, 2026, 12:05 p.m.