Triple
T13621622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janine Turner |
E325470
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Janine Turner |
E325470
|
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: Janine Turner | Statement: [Janine Turner, name, Janine Turner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Janine Turner Context triple: [Janine Turner, name, Janine Turner]
-
A.
Janine Turner
chosen
Janine Turner is an American actress best known for her roles in the television series "Northern Exposure" and the action film "Cliffhanger."
-
B.
Tyne Daly
Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
-
C.
Janet Robins
Janet Robins is an individual known for having attended St. Clare's School.
-
D.
Lucinda Jenney
Lucinda Jenney is an American character actress known for her versatile supporting roles in films and television since the 1980s.
-
E.
Tamara Tunie
Tamara Tunie is an American actress and director best known for her long-running role as medical examiner Melinda Warner on the television series "Law & Order: Special Victims Unit."
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0b0c9008190836242da2d6a8cbe |
completed | April 12, 2026, 2:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1b4ae588190b293c71312ee4037 |
completed | May 6, 2026, 8:16 p.m. |
Created at: April 9, 2026, 9:50 p.m.