Triple
T12120653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erin Reagan |
E288683
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Robin Green |
E268685
|
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: Robin Green | Statement: [Erin Reagan, creator, Robin Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robin Green Context triple: [Erin Reagan, creator, Robin Green]
-
A.
Robin Green
chosen
Robin Green is an American television writer and producer best known for her work on acclaimed series such as *The Sopranos*.
-
B.
Daniel Green
Daniel Green is a music producer known for his work on the track "Paradise."
-
C.
Richard Green
Richard Green was an American boxing referee best known for officiating major heavyweight bouts, including the 1980 title fight between Larry Holmes and Muhammad Ali.
-
D.
Martin Green
Martin Green is a renowned Australian engineer and solar energy researcher recognized as a leading pioneer in photovoltaic technology.
-
E.
Sam Greenfield
Sam Greenfield is the perpetually unlucky young woman who becomes the central heroine of the animated fantasy film "Luck," navigating a secret world of good and bad fortune.
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91577a03c81909add7a5d7324a648 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f684172c81908b77daa243dc8ed8 |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:49 p.m.