Triple
T15941349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bekaa Governorate |
E386570
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Zahlé |
E531935
|
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: Zahlé | Statement: [Bekaa Governorate, hasCity, Zahlé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zahlé Context triple: [Bekaa Governorate, hasCity, Zahlé]
-
A.
Zahlé
chosen
Zahlé is a major Lebanese city known for its vineyards, cuisine, and scenic location along the Berdawni River in the Beqaa region.
-
B.
Znamianka
Znamianka is a city in central Ukraine that serves as an important regional railway junction and administrative center within Kirovohrad Oblast.
-
C.
Baníkov
Baníkov is a prominent peak in Slovakia’s Western Tatras, popular with hikers for its rugged ridges and panoramic alpine views.
-
D.
Krompachy
Krompachy is a small industrial town in eastern Slovakia known historically for its ironworks and metalworking industry.
-
E.
Kohout
Kohout is a Czech surname borne by various notable individuals in fields such as literature, economics, and sports.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156ce0230819089a20114a755a75a |
completed | April 16, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe7455c48190bfad24eb8905426d |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:53 a.m.