Triple
T21746012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rob Simonsen |
E536788
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Tully |
—
|
NE NERFINISHED |
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: Tully | Statement: [Rob Simonsen, notableWork, Tully]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tully Context triple: [Rob Simonsen, notableWork, Tully]
-
A.
Tully
Tully is a central figure in Leonard Gardner’s novel and its film adaptation "Fat City," portrayed as a struggling boxer navigating hardship and disillusionment in Stockton, California.
-
B.
Tully
chosen
Tully is a 2018 comedy-drama film starring Charlize Theron as an overwhelmed mother who forms an unexpected bond with her night nanny.
-
C.
Tully
Tully is a rural town in Far North Queensland, Australia, known for its high rainfall and sugar cane farming.
-
D.
Tully
Tully is a surname most notably associated with Brent Tully, an astronomer known for his work on the large-scale structure of the universe and galaxy distributions.
-
E.
Tuthill
Tuthill is a surname most notably associated with William Burnet Tuthill, the American architect who designed Carnegie Hall in New York City.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a76540c8190b91a67f4a70869fb |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 16, 2026, 6:49 p.m.