Triple
T19150243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garowe Airport |
E468784
|
entity |
| Predicate | hasCityServed |
P3936
|
FINISHED |
| Object | Garowe |
—
|
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: Garowe | Statement: [Garowe Airport, hasCityServed, Garowe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garowe Context triple: [Garowe Airport, hasCityServed, Garowe]
-
A.
Garowe
chosen
Garowe is the administrative and economic center of the autonomous Puntland region in northeastern Somalia.
-
B.
Bazarak
Bazarak is a small town in northeastern Afghanistan that serves as the administrative and political center of the strategically significant Panjshir region.
-
C.
Zhob
Zhob is a town and district in northwestern Balochistan, Pakistan, known historically as a strategic frontier outpost and regional trade center near the Afghan border.
-
D.
Zangilan
Zangilan is a town in southwestern Azerbaijan that serves as an administrative and transport hub near the borders with Armenia and Iran.
-
E.
Khansar
Khansar is a historic city in central Iran known for its traditional architecture, cool climate, and production of honey and dairy products.
- 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_69d8dd084ff48190ac0f8c46ee722629 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e97c42348190875a2f5b5bc0b99e |
completed | April 20, 2026, 8:53 a.m. |
Created at: April 10, 2026, 12:06 p.m.