Triple
T3525931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drava |
E74537
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Osijek |
E133925
|
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: Osijek | Statement: [Drava, flowsThroughCity, Osijek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osijek Context triple: [Drava, flowsThroughCity, Osijek]
-
A.
Osijek
chosen
Osijek is a prominent city in eastern Croatia known as an economic, cultural, and educational center of the Slavonia region.
-
B.
Zagreb
Zagreb is the capital and largest city of Croatia, known as a political, cultural, and economic hub in the Balkans.
-
C.
Barajevo
Barajevo is a suburban municipality of Belgrade, Serbia, located in the southern part of the city’s administrative area.
-
D.
Banja Luka
Banja Luka is the second-largest city of Bosnia and Herzegovina and the administrative center of the Republika Srpska entity, known for its riverside setting, Austro-Hungarian architecture, and cultural life.
-
E.
Zrenjanin
Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
- 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_69ad85d0c5488190a3d8e02ebd01a1aa |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc6a8d0c819094d38b9c47fb67b4 |
completed | March 8, 2026, 6:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bca41a88190b5550b9c1e763092 |
completed | March 13, 2026, 4 a.m. |
Created at: March 8, 2026, 3:19 p.m.