Triple
T17941395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agha |
E448595
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Aga |
—
|
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: Aga | Statement: [Agha, hasVariant, Aga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aga Context triple: [Agha, hasVariant, Aga]
-
A.
Aga
chosen
Aga is a common Polish diminutive form of the female given name Agnieszka.
-
B.
Aga
Aga is a city in Egypt’s Dakahlia Governorate, known as one of the region’s principal urban centers in the Nile Delta.
-
C.
Aga
Aga is a rural town in Japan known for its mountainous landscapes and location within Niigata Prefecture on the island of Honshu.
-
D.
Agta
The Agta are an indigenous Negrito people of the Philippines known for their traditionally nomadic, forest-based lifestyle and rich oral traditions.
-
E.
Serabi
Serabi is a traditional Indonesian pancake-like cake made from rice flour and coconut milk, often served with sweet toppings or syrup.
- 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4ad95f4608190b1ebb45944218f07 |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, 10:21 a.m.