Triple
T15090016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Maross |
E360389
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Joe Maross |
E360389
|
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: Joe Maross | Statement: [Joe Maross, name, Joe Maross]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Maross Context triple: [Joe Maross, name, Joe Maross]
-
A.
Joe Maross
chosen
Joe Maross was an American character actor known for his numerous film and television roles from the 1950s through the 1980s, including appearances in classic series like "The Twilight Zone."
-
B.
Brian DeMarco
Brian DeMarco is an American experimental physicist known for his work on ultracold atomic gases and quantum many-body physics.
-
C.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
-
D.
Troy Kinney
Troy Kinney was an American etcher, illustrator, and author known for his detailed prints and artworks, particularly those depicting dance and theatrical subjects.
-
E.
Jarin Blaschke
Jarin Blaschke is an American cinematographer best known for his stark, atmospheric black-and-white work on films like "The Lighthouse."
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00277ea808190be3f002a8316eff1 |
completed | April 15, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec87ad03c8190b8a77e8eca9caf4d |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 3:04 a.m.