Triple
T21363286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harvey Lembeck |
E526838
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Michael Lembeck |
—
|
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: Michael Lembeck | Statement: [Harvey Lembeck, child, Michael Lembeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Lembeck Context triple: [Harvey Lembeck, child, Michael Lembeck]
-
A.
Michael Lembeck
chosen
Michael Lembeck is an American director and actor best known for his work on family comedies and popular television series such as "Friends."
-
B.
Michael Kelbaugh
Michael Kelbaugh is a video game producer and longtime Nintendo collaborator best known for his leadership at Retro Studios on major titles in the Donkey Kong Country series.
-
C.
Ken Lemberger
Ken Lemberger is a film producer known for his work on the 2006 adaptation of "All the King's Men."
-
D.
Daniel Blumberg
Daniel Blumberg is a British musician and composer known for his experimental work in indie rock and film scores.
-
E.
Michael Lucker
Michael Lucker is an American screenwriter known for his work on animated and family films, including contributions to Disney projects.
- 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_69e0b51d8a308190b09113b3b3f9bc15 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b06cbcb481909ec9014da3b0a18a |
completed | April 22, 2026, 11:26 a.m. |
Created at: April 16, 2026, 5:08 p.m.