Triple
T4876491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eritrean nakfa |
E109216
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Nakfa |
E416300
|
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: Nakfa | Statement: [Eritrean nakfa, namedAfter, Nakfa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nakfa Context triple: [Eritrean nakfa, namedAfter, Nakfa]
-
A.
Nakfa
chosen
Nakfa is a historically significant town in Eritrea known for its role in the independence struggle and for lending its name to the country’s national currency.
-
B.
Rahanweyn
Rahanweyn is a major dialect (often considered a distinct variety) of the Somali language spoken primarily by the Rahanweyn clan families in southern Somalia.
-
C.
Rahan
Rahan is a rural village in County Offaly, Ireland, known for its historic religious and educational institutions and its proximity to the Grand Canal.
-
D.
Buhera
Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
-
E.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
- 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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6dbbc734819083b28a022e5690d6 |
completed | March 20, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be67fc50cc819083a475a1de914670 |
completed | March 21, 2026, 9:42 a.m. |
Created at: March 20, 2026, 1:27 p.m.