Triple
T19424893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Sea trade network |
E485955
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Varna |
—
|
NE NERFINISHED |
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: Varna | Statement: [Black Sea trade network, hasPart, Varna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Varna Context triple: [Black Sea trade network, hasPart, Varna]
-
A.
Varna
chosen
Varna is a major Bulgarian city on the Black Sea coast known as an important economic, cultural, and maritime center.
-
B.
Velingrad
Velingrad is a renowned Bulgarian spa town famous for its numerous mineral springs and status as one of the country’s leading balneological and wellness resorts.
-
C.
Ruse
Ruse is a residential suburb in the Macarthur region of Sydney, New South Wales, Australia.
-
D.
Ruse
Ruse is a major Bulgarian city and river port on the Danube, known for its elegant architecture and role as an important economic and transport hub.
-
E.
Silistra
Silistra is a historic city in northeastern Bulgaria on the Danube River, known as an important cultural and economic center of the Dobruja region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63217bd2c81909e216e13aa4c487d |
completed | April 20, 2026, 2:03 p.m. |
Created at: April 10, 2026, 1:37 p.m.