Triple
T10559441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afar Region |
E249175
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Semera |
E870586
|
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: Semera | Statement: [Afar Region, largestCity, Semera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Semera Context triple: [Afar Region, largestCity, Semera]
-
A.
Semera
chosen
Semera is a planned town in northeastern Ethiopia that serves as the administrative and economic center of the Afar Region.
-
B.
Serabi
Serabi is a traditional Indonesian pancake-like cake made from rice flour and coconut milk, often served with sweet toppings or syrup.
-
C.
Temara
Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
-
D.
Serein
Serein is a river in central France that flows through the Burgundy region before joining the Yonne River.
-
E.
Salora
Salora was a prominent Finnish electronics manufacturer best known for producing televisions and radios, and it played a key role in the industrial history of Salo, Finland.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5271e65688190bcf7931373d87f94 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b38b4c081908cc2816144c23152 |
completed | April 10, 2026, 7:10 p.m. |
Created at: April 6, 2026, 12:35 p.m.