Triple
T16256295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samtredia railway station |
E394636
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object | Senaki |
E116227
|
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: Senaki | Statement: [Samtredia railway station, connectsWith, Senaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senaki Context triple: [Samtredia railway station, connectsWith, Senaki]
-
A.
Senaki
chosen
Senaki is a town in western Georgia that serves as an important local administrative and transportation center in the Samegrelo region.
-
B.
Nabaloi
Nabaloi is an Austronesian language spoken by the Ibaloi people of the northern Philippines, particularly in the Benguet region of Luzon.
-
C.
Tserona
Tserona is a town in southern Eritrea located within the Debub Region, known historically for its strategic position near the Ethiopian border.
-
D.
Girga
Girga is an ancient town in Upper Egypt, historically significant as a regional center along the Nile.
-
E.
Kabaena
Kabaena is an island in Indonesia known for its location off the coast of Sulawesi and its mix of coastal and hilly landscapes.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2459a48f081909c76b38741b8f04e |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017b3b48c8190ad0043d730b1da35 |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:04 a.m.