Triple
T12734186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Mariupol (2022) |
E304319
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Mariupol |
E54211
|
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: Mariupol | Statement: [Siege of Mariupol (2022), location, Mariupol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariupol Context triple: [Siege of Mariupol (2022), location, Mariupol]
-
A.
Mariupol
chosen
Mariupol is a major industrial city in southeastern Ukraine known for its strategic port on the Sea of Azov and its significant role in recent military conflicts.
-
B.
Kramatorsk
Kramatorsk is an industrial city in eastern Ukraine that has become a key administrative and strategic center in the Donbas region.
-
C.
Donetsk
Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
-
D.
Bucha
Bucha is a suburban town northwest of Kyiv in northern Ukraine, internationally known as the site of alleged war crimes and civilian massacres during the 2022 Russian invasion.
-
E.
Melitopol
Melitopol is a strategically important industrial and transportation hub in southeastern Ukraine, known for its agricultural production and key road and rail connections.
- 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9646902408190b29268d864833b80 |
completed | April 10, 2026, 8:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e25d24e481908253e1af630835f1 |
completed | May 3, 2026, 5:51 a.m. |
Created at: April 9, 2026, 5:26 p.m.