Triple
T21638827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stalker |
E534032
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Liz Friedlander |
—
|
NE NERFINISHED |
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: Liz Friedlander | Statement: [Stalker, executiveProducer, Liz Friedlander]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liz Friedlander Context triple: [Stalker, executiveProducer, Liz Friedlander]
-
A.
Liz Friedlander
chosen
Liz Friedlander is an American director best known for her work on music videos for major pop and rock artists as well as episodes of popular television series.
-
B.
Liz Friedman
Liz Friedman is an American television writer and producer known for her work on series such as The Good Doctor, House, and Xena: Warrior Princess.
-
C.
Susan Friedlander
Susan Friedlander is an American mathematician known for her contributions to fluid dynamics and partial differential equations, as well as for her leadership roles in the mathematical community.
-
D.
Susan Littenberg
Susan Littenberg is a film editor known for her work on feature films such as the teen comedy "Easy A."
-
E.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c465ae7481908577b7209fdb2a77 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef538fb12481908f70ad8dbe1d99e4 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 16, 2026, 6:35 p.m.