Triple
T13077602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soroti |
E329617
|
entity |
| Predicate | roadConnectionTo |
P9041
|
FINISHED |
| Object | Lira |
E329618
|
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: Lira | Statement: [Soroti, roadConnectionTo, Lira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lira Context triple: [Soroti, roadConnectionTo, Lira]
-
A.
Lira
chosen
Lira is a major town in northern Uganda that serves as an important commercial and administrative center for the surrounding region.
-
B.
Okyar
Okyar is a Turkish surname most notably associated with Fethi Okyar, an important early 20th-century Turkish statesman and diplomat.
-
C.
Meram
Meram is a central district and municipality of Konya in Turkey, known for its historic neighborhoods, gardens, and cultural heritage.
-
D.
Darıca
Darıca is a coastal town and district in northwestern Turkey, situated on the Sea of Marmara and known for its zoo, recreation areas, and proximity to Istanbul.
-
E.
Ergene
Ergene is a district and municipality in Turkey’s Tekirdağ Province, located in the European (Thrace) part of the country and known for its industrial activity.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9811828448190ac6ddd3e9c221251 |
completed | April 10, 2026, 11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d60aaac48190b5b724a19cad5279 |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 9:01 p.m.