Triple
T13258677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Lowell |
E315730
|
entity |
| Predicate | playedCharacter |
P1507
|
FINISHED |
| Object | Dell Parker |
E688870
|
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: Dell Parker | Statement: [Chris Lowell, playedCharacter, Dell Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dell Parker Context triple: [Chris Lowell, playedCharacter, Dell Parker]
-
A.
Dell Parker
chosen
Dell Parker is a character on the medical drama series "Private Practice," known as the Oceanside Wellness Group's receptionist and aspiring midwife.
-
B.
Dell Scott
Dell Scott is the charismatic ex-convict caregiver portrayed by Kevin Hart in the 2017 film "The Upside."
-
C.
Dell Henderson
Dell Henderson was a Canadian-born American actor and director prominent in early silent films and stage productions.
-
D.
Erik Dellums
Erik Dellums is an American actor and voice actor known for roles in projects like "The Wire" and the video game "Fallout 3."
-
E.
Roger Donley
Roger Donley is a film editor known for his work on the animated feature "Snoopy, Come Home."
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f778088819082b8a596c04bfe02 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78acfd0e88190954533a7f282d83a |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:25 p.m.