Triple
T14398015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Multiplicity |
E357000
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Bonnie Koehler |
E1048500
|
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: Bonnie Koehler | Statement: [Multiplicity, editedBy, Bonnie Koehler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bonnie Koehler Context triple: [Multiplicity, editedBy, Bonnie Koehler]
-
A.
Bonnie Koehler
Bonnie Koehler is a film editor best known for her work on the 1995 adaptation of "Freaky Friday."
-
B.
Bonnie Koehler
chosen
Bonnie Koehler is an editor known for her work on the film "Beethoven's 2nd."
-
C.
Betsy Koch
Betsy Koch is a film producer known for her work on the darkly comedic horror-thriller "The Menu."
-
D.
Colleen Ahland
Colleen Ahland is a linguist known for her research on the Koman languages of Ethiopia and Sudan, focusing on their documentation, description, and classification.
-
E.
Cathy Kuhlmeier
Cathy Kuhlmeier is a former high school student who became known for challenging school censorship of a student newspaper in the landmark U.S. Supreme Court case Hazelwood School District v. Kuhlmeier.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9083f9d081908fe5c99655c410b3 |
completed | April 14, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180bd1e5c8190a6a96581ce8a37de |
completed | May 11, 2026, 7:09 a.m. |
Created at: April 10, 2026, 1:17 a.m.