Triple
T23330208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bangladesh |
E591423
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Bossy |
—
|
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: Bossy | Statement: [Bangladesh, notableWork, Bossy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bossy Context triple: [Bangladesh, notableWork, Bossy]
-
A.
Bossy
Bossy is a surname most famously associated with Mike Bossy, the legendary Canadian ice hockey goal-scorer for the New York Islanders.
-
B.
Bossy
chosen
"Bossy" is a 2006 hip hop/R&B song by Kelis featuring Too $hort, known for its confident lyrics and catchy, bass-heavy production.
-
C.
Lady Boss
Lady Boss is a bestselling novel by Jackie Collins that continues her glamorous, scandal-filled tales of power, sex, and intrigue in Hollywood.
-
D.
Sassy
Sassy is the nickname of Sarah Vaughan, the legendary American jazz singer renowned for her rich, expressive voice and virtuosic vocal technique.
-
E.
Sassy
Sassy was an influential American teen magazine from the late 1980s and early 1990s known for its feminist, alternative take on youth culture and media.
- 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197ece360819099da8443dd86a355 |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:15 p.m.