Triple
T9815914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Hahn |
E238403
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Don Hahn |
E238403
|
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: Don Hahn | Statement: [Don Hahn, name, Don Hahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Hahn Context triple: [Don Hahn, name, Don Hahn]
-
A.
Don Hahn
chosen
Don Hahn is an American film producer best known for overseeing several of Disney’s most acclaimed animated features, including Beauty and the Beast and The Lion King.
-
B.
Joe Hahn
Joe Hahn is an American musician and DJ best known as the turntablist and sampler for the rock band Linkin Park.
-
C.
Hank Corwin
Hank Corwin is an acclaimed American film editor known for his impressionistic, nonlinear cutting style on films such as The Tree of Life, The Big Short, and Vice.
-
D.
Lew Hahn
Lew Hahn is a recording engineer known for his work on notable music projects such as the song "I'm Every Woman."
-
E.
David Mann
David Mann is the harried, everyman motorist relentlessly terrorized by a mysterious truck driver in Steven Spielberg’s thriller film "Duel."
- 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb2f341648190bf8343e1124085cb |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eacbc2348190bac1cc7f41a389b9 |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:30 p.m.