Triple
T9913314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of 1000 Corpses |
E185800
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Kathryn Himoff |
E689066
|
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: Kathryn Himoff | Statement: [House of 1000 Corpses, editedBy, Kathryn Himoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kathryn Himoff Context triple: [House of 1000 Corpses, editedBy, Kathryn Himoff]
-
A.
Kathryn Himoff
chosen
Kathryn Himoff is a film editor known for her work on feature films, including editing the biographical drama "Pollock."
-
B.
Kathryn Chetkovich
Kathryn Chetkovich is an American writer and essayist known for her fiction and for her widely discussed essay about envy and literary success.
-
C.
Kirsten Fudeman
Kirsten Fudeman is a linguist and scholar known for her collaborative work with Mark Aronoff in the field of morphology and the history of linguistic thought.
-
D.
Claire Lademacher
Claire Lademacher is a German-born bioethics researcher who became a member of the Luxembourg royal family through her marriage to Prince Félix.
-
E.
Michelle Mylett
Michelle Mylett is a Canadian actress best known for playing Katy on the comedy series "Letterkenny."
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb53a300481909d917e487d8aab56 |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d354c521dc819084b09c9a57c1a26c |
completed | April 6, 2026, 6:37 a.m. |
Created at: March 30, 2026, 8:41 p.m.