Triple
T9078477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Lorkowski |
E217549
|
entity |
| Predicate | hasMother |
P1909
|
FINISHED |
| Object | Rose Lorkowski |
E217548
|
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: Rose Lorkowski | Statement: [Joe Lorkowski, hasMother, Rose Lorkowski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose Lorkowski Context triple: [Joe Lorkowski, hasMother, Rose Lorkowski]
-
A.
Rose Lorkowski
chosen
Rose Lorkowski is the struggling single mother and former high school cheerleader who starts a crime-scene cleanup business in the film "Sunshine Cleaning."
-
B.
Loralee Czuchna
Loralee Czuchna is best known as the second wife of American actor and comedian Don Knotts.
-
C.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
-
D.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
-
E.
Anna Nolin
Anna Nolin is an American educator and school district leader who serves as superintendent of the Newton Public Schools in Massachusetts.
- 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_69ca83d6c14c8190bc056d927f00a2a2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc95c7d3688190a4c1c6a92965eae4 |
completed | April 1, 2026, 3:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d017baebb881908cfecd3438a17166 |
completed | April 3, 2026, 7:40 p.m. |
Created at: March 30, 2026, 7:12 p.m.