Triple
T7497625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ned and Stacey |
E177173
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Greg Germann |
E349927
|
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: Greg Germann | Statement: [Ned and Stacey, starring, Greg Germann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greg Germann Context triple: [Ned and Stacey, starring, Greg Germann]
-
A.
Greg Germann
chosen
Greg Germann is an American actor best known for his role as the uptight, comedic lawyer Richard Fish on the television series "Ally McBeal."
-
B.
Mark Bittner
Mark Bittner is an American writer and former street musician best known for his close relationship with and documentation of a flock of wild parrots in San Francisco’s Telegraph Hill neighborhood.
-
C.
Mark Geiger
Mark Geiger is a retired American soccer referee known for officiating at Major League Soccer’s highest levels and multiple FIFA World Cups.
-
D.
Matt Greenberg
Matt Greenberg is a screenwriter and film producer known for his work on various horror and thriller projects in American cinema.
-
E.
Bryan Greenberg
Bryan Greenberg is an American actor and singer best known for his roles in television series like "One Tree Hill" and "How to Make It in America," as well as various romantic comedies.
- 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_69c69f2583808190bd1a4936c42a5815 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5963d98819098275b161848d2d4 |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c846043ed48190b6fa45ed0e70b4d3 |
completed | March 28, 2026, 9:20 p.m. |
Created at: March 27, 2026, 3:44 p.m.