Triple
T6977523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Çanakkale Province |
E161750
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object | Lapseki |
E367393
|
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: Lapseki | Statement: [Çanakkale Province, hasMajorTown, Lapseki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lapseki Context triple: [Çanakkale Province, hasMajorTown, Lapseki]
-
A.
Lapseki
chosen
Lapseki is a town and district in Çanakkale Province in northwestern Turkey, situated on the Asian shore of the Dardanelles Strait.
-
B.
Kalsa
Kalsa is a historic district of Palermo, Italy, known for its Arab-Norman heritage, medieval streets, and vibrant cultural life.
-
C.
Larevat
Larevat is an Oceanic language spoken in Vanuatu, closely related to and geographically near the Uripiv-Wala-Rano-Atchin language cluster.
-
D.
Kalkan
Kalkan is a picturesque seaside town on Turkey’s Mediterranean coast, known for its historic architecture, steep cobbled streets, and upscale tourism.
-
E.
Nalut
Nalut is a town in western Libya situated in the Nafusa Mountains, known for its Amazigh (Berber) heritage and historic hilltop granaries.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db68d25c8190a1776908619ad979 |
completed | March 27, 2026, 7:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a0ad57c81909aec9f619dc68bd7 |
completed | March 28, 2026, 5:41 a.m. |
Created at: March 27, 2026, 2:31 p.m.