Triple
T3975374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vladimir Vernadsky |
E85625
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Kiev |
E17733
|
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: Kiev | Statement: [Vladimir Vernadsky, workLocation, Kiev]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiev Context triple: [Vladimir Vernadsky, workLocation, Kiev]
-
A.
Kyiv
chosen
Kyiv is the capital and largest city of Ukraine, serving as its political, cultural, and economic center.
-
B.
Kharkiv
Kharkiv is Ukraine’s second-largest city and a major industrial, cultural, and educational center in the northeast of the country.
-
C.
Dnipro
Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
-
D.
Odessa
Odessa is a mid-sized city in western Texas known for its oil industry, high school football culture, and role in the Permian Basin energy region.
-
E.
Odessa
Odessa is a central, devoutly religious housekeeper in James Baldwin’s play "The Amen Corner," known for her loyalty and moral grounding amid the story’s family and church conflicts.
- 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_69aed93908348190a26c8aaf4fab3e86 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef9b511f88190afca12c77481b344 |
completed | March 9, 2026, 4:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd6f783430819094df0bc715f9236d |
completed | March 20, 2026, 4:02 p.m. |
Created at: March 9, 2026, 3:33 p.m.