Triple
T18883662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baniyas |
E461897
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Tartus |
—
|
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: Tartus | Statement: [Baniyas, near, Tartus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tartus Context triple: [Baniyas, near, Tartus]
-
A.
Tartus
chosen
Tartus is a major Syrian port city on the Mediterranean coast that hosts Russia’s only naval facility outside the former Soviet Union.
-
B.
Latakia
Latakia is a major port city on Syria's Mediterranean coast and an important economic and cultural center for the country.
-
C.
Aleppo
Aleppo is an ancient and historically significant city in northern Syria, renowned for its rich cultural heritage, medieval architecture, and role as a major trading hub along the Silk Road.
-
D.
Port of Latakia
Port of Latakia is Syria’s principal seaport on the Mediterranean, serving as a major hub for the country’s maritime trade and cargo traffic.
-
E.
Anamur
Anamur is a coastal town and district in southern Turkey known for its banana production and historic Mamure Castle.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3d3dcec8190a468162c6a4482f6 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.