Triple
T3772092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juno |
E83220
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Dana E. Glauberman |
E242460
|
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: Dana E. Glauberman | Statement: [Juno, editor, Dana E. Glauberman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dana E. Glauberman Context triple: [Juno, editor, Dana E. Glauberman]
-
A.
Dana Glauberman
chosen
Dana Glauberman is an American film editor known for her work on numerous high-profile feature films and collaborations with director Jason Reitman.
-
B.
Evan A. Lottman
Evan A. Lottman was an American film editor known for his work on acclaimed films including the adaptation of "Sophie's Choice."
-
C.
Jay O. Rothman
Jay O. Rothman is an American attorney and academic leader who serves as president of the University of Wisconsin System.
-
D.
Michael A. Gottlieb
Michael A. Gottlieb is a physicist and editor known for his work on Richard Feynman–related educational materials, including co-editing "Feynman’s Tips on Physics."
-
E.
Michael P. Brenner
Michael P. Brenner is an American applied mathematician and physicist known for his influential work in fluid dynamics and complex systems.
- 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_69ad8b235e608190b5a2b1d1bfcef50b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc3219b881908a2f82126f9a679d |
completed | March 8, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f59074e881908d346937da0b056e |
completed | March 14, 2026, 11:56 p.m. |
Created at: March 8, 2026, 3:36 p.m.