Triple
T2770350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abia State |
E61439
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Aba |
E82542
|
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: Aba | Statement: [Abia State, hasCity, Aba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aba Context triple: [Abia State, hasCity, Aba]
-
A.
Aba
chosen
Aba is a major commercial and industrial city in southeastern Nigeria, known for its vibrant markets and manufacturing activities.
-
B.
Aba
Aba is a small town in central Hungary located within Fejér County, known for its rural character and agricultural surroundings.
-
C.
Afula
Afula is a city in northern Israel often referred to as the "Capital of the Jezreel Valley," serving as a regional commercial and transportation hub.
-
D.
Beni
Beni is a town in western Nepal that serves as a gateway to the Dhaulagiri and Annapurna mountain regions.
-
E.
Beni
Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd690b24819095647dd4a4f902bb |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce89a144819096f9230c8d95ce3f |
completed | March 10, 2026, 7:55 a.m. |
Created at: March 6, 2026, 9:57 p.m.