Triple
T515698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebanon |
E10701
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Beirut |
E4667
|
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: Beirut | Statement: [Lebanon, capital, Beirut]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beirut Context triple: [Lebanon, capital, Beirut]
-
A.
Beirut
chosen
Beirut is the capital and largest city of Lebanon, known as a historic cultural, commercial, and financial hub of the Eastern Mediterranean.
-
B.
Tartus
Tartus is a major Syrian port city on the Mediterranean coast that hosts Russia’s only naval facility outside the former Soviet Union.
-
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.
Haifa
Haifa is a major Israeli city on the Mediterranean coast, known for its significant port, mixed Jewish-Arab population, and the terraced Baháʼí Gardens on Mount Carmel.
-
E.
Tunis
Tunis is the capital and largest city of Tunisia, serving as a major political, economic, and cultural center in the Arab world.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1836b688190a60cc901a8724159 |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4ab1dc61081908d7215b68f95b2ba |
completed | March 1, 2026, 9:09 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.