Triple
T3401070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Natan Sharansky |
E71652
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Donetsk |
E110135
|
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: Donetsk | Statement: [Natan Sharansky, placeOfBirth, Donetsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donetsk Context triple: [Natan Sharansky, placeOfBirth, Donetsk]
-
A.
Donetsk
chosen
Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
-
B.
Donetsk Oblast
Donetsk Oblast is an industrial and heavily urbanized region in eastern Ukraine, historically known for coal mining and metallurgy and currently a focal point of the Russo-Ukrainian conflict.
-
C.
Kherson
Kherson is a port city in southern Ukraine near the Black Sea, historically significant as a shipbuilding and industrial center and strategically important due to its location on the Dnieper River.
-
D.
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.
-
E.
Mykolaiv
Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
- 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_69ad85aac4808190a092c9cc8911f584 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8c80e0081909c3d5f95ab6f55a0 |
completed | March 8, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5281d59c881909a23b5d7e7eff307 |
completed | March 14, 2026, 9:19 a.m. |
Created at: March 8, 2026, 3:14 p.m.