Triple
T16276854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dillon |
E395150
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Melinda Dillon |
E304877
|
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: Melinda Dillon | Statement: [Dillon, hasNotableBearer, Melinda Dillon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melinda Dillon Context triple: [Dillon, hasNotableBearer, Melinda Dillon]
-
A.
Melinda Dillon
chosen
Melinda Dillon was an American actress best known for her Oscar-nominated role in "Close Encounters of the Third Kind" and for playing the mother in the holiday classic "A Christmas Story."
-
B.
Melissa Cobb
Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
-
C.
Melinda Kinnaman
Melinda Kinnaman is an American-Swedish actress known for her work in Swedish film, television, and theatre, including her breakthrough role in the film "My Life as a Dog."
-
D.
Melissa Reeves
Melissa Reeves is a fictional character in the 1996 disaster film "Twister," where she begins the story as the fiancée of storm chaser Bill Harding.
-
E.
Colleen Ventimiglia
Colleen Ventimiglia is a screenwriter best known for her work on the animated Disney sequel "Cinderella III: A Twist in Time."
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2460ea5a4819099f779288a754c8d |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0091855ab48190a14ad7df9cd806ad |
completed | May 10, 2026, 2:09 p.m. |
Created at: April 10, 2026, 5:05 a.m.