Triple
T12333466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The In Crowd |
E294019
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Lori Heuring |
E978312
|
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: Lori Heuring | Statement: [The In Crowd, castMember, Lori Heuring]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lori Heuring Context triple: [The In Crowd, castMember, Lori Heuring]
-
A.
Lori Heuring
chosen
Lori Heuring is an American actress best known for her lead role in the psychological thriller film "The In Crowd" (2000).
-
B.
Tamara Diane Miller
Tamara Diane Miller is a member of the extended Disney family, descended from the lineage of Walt Disney’s relatives.
-
C.
Linda Kay Cooper
Linda Kay Cooper is known as a former spouse of James William Johnson.
-
D.
Gail Strickland
Gail Strickland is an American character actress known for her work in film and television since the 1970s.
-
E.
Gail C. Murphy
Gail C. Murphy is a prominent Canadian computer scientist known for her influential research in software engineering, particularly in improving developer productivity and software evolution.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f64ad20819080d99e57833b4b51 |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6346c06208190b4e39fcbdb6a06fa |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:53 p.m.