Triple
T3858104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bilen people |
E90067
|
entity |
| Predicate | autonym |
P1435
|
FINISHED |
| Object | Bilen |
E90064
|
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: Bilen | Statement: [Bilen people, autonym, Bilen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bilen Context triple: [Bilen people, autonym, Bilen]
-
A.
Bilen
chosen
Bilen is a Cushitic language spoken primarily by the Bilen people in central Eritrea.
-
B.
L’Auto
L’Auto was a French sports newspaper best known for creating and organizing the Tour de France.
-
C.
The Diesel
The Diesel is the nickname of Pro Football Hall of Fame running back John Riggins, renowned for his powerful, hard-charging rushing style with the Washington Redskins.
-
D.
Niva
Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
-
E.
Hella
Hella is a central character in James Baldwin’s novel "Giovanni’s Room," serving as the protagonist’s fiancée and a key figure in exploring themes of sexuality, identity, and societal expectations.
- 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_69aed95b3c088190a8f85d19e6070599 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec1e68f88190941c39221486f6ae |
completed | March 9, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b504228220819082e11b316ba79b08 |
completed | March 14, 2026, 6:45 a.m. |
Created at: March 9, 2026, 3:19 p.m.