Triple
T6291867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Officer |
E141036
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Deezle |
E527992
|
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: Deezle | Statement: [Mrs. Officer, producer, Deezle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deezle Context triple: [Mrs. Officer, producer, Deezle]
-
A.
Deezle
chosen
Deezle is an American hip-hop and R&B record producer best known for his work with Lil Wayne, including contributions to the hit album "Tha Carter III."
-
B.
Doozer
Doozer is a television production company founded by Bill Lawrence, best known for producing series such as Scrubs, Cougar Town, and Ted Lasso.
-
C.
Garthdee
Garthdee is a riverside area in the southwest of Aberdeen, Scotland, known for hosting the main campus of Robert Gordon University.
-
D.
Denguin
Denguin is a small commune in southwestern France, located in the Pyrénées-Atlantiques department in the Nouvelle-Aquitaine region.
-
E.
Zeb
Zeb is a central character in Margaret Atwood's dystopian novel "MaddAddam," known for his complex past and role in the post-apocalyptic narrative.
- 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_69c008cdf2ac8190bb640c94478fb4ed |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0641ea224819097b6962e6014c690 |
completed | March 22, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e41fb4708190b6b1433e27c9276e |
completed | March 27, 2026, 1:57 a.m. |
Created at: March 22, 2026, 4:27 p.m.