Triple
T4366019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D'Lila Star Combs |
E98773
|
entity |
| Predicate | parent |
P120
|
FINISHED |
| Object | Kim Porter |
E458176
|
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: Kim Porter | Statement: [D'Lila Star Combs, parent, Kim Porter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Porter Context triple: [D'Lila Star Combs, parent, Kim Porter]
-
A.
Kim Porter
chosen
Kim Porter was an American model and actress best known for her longtime relationship with Sean "Diddy" Combs and her work in fashion and entertainment.
-
B.
Lori Marshall
Lori Marshall is an American television writer and author, known for her work on sitcoms and for collaborating on books about and with her father, filmmaker Garry Marshall.
-
C.
Stephany Folsom
Stephany Folsom is an American screenwriter best known for co-writing Pixar’s animated film "Toy Story 4" and contributing to various high-profile film and television projects.
-
D.
Laura Harris
Laura Harris is a Canadian actress known for her roles in films like "The Faculty" and TV series such as "24" and "Dead Like Me."
-
E.
Larissa Howard
Larissa Howard is known as the daughter of British military historian and politician Michael Howard.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35200263081909bb326a4d7a8db99 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be03295174819096d299919a6d7e57 |
completed | March 21, 2026, 2:32 a.m. |
Created at: March 12, 2026, 11:17 p.m.