Triple
T12248031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Crow |
E291900
|
entity |
| Predicate | hasRelativeByMarriage |
P7844
|
FINISHED |
| Object | Danny Bennett |
E735817
|
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: Danny Bennett | Statement: [Susan Crow, hasRelativeByMarriage, Danny Bennett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danny Bennett Context triple: [Susan Crow, hasRelativeByMarriage, Danny Bennett]
-
A.
Danny Bennett
chosen
Danny Bennett is an American music producer and longtime manager best known for guiding the later career of his father, legendary singer Tony Bennett.
-
B.
Dan Dugmore
Dan Dugmore is an American session musician and steel guitarist known for his work with prominent country and rock artists.
-
C.
Finley Hobbins
Finley Hobbins is a young American actor best known for his role in Disney’s live-action adaptation of "Dumbo" (2019).
-
D.
Tom Natsworthy
Tom Natsworthy is the young, idealistic historian’s apprentice who becomes an unlikely hero in the post-apocalyptic, mobile-city world of Mortal Engines.
-
E.
Charlie Jaffey
Charlie Jaffey is a high-powered, principled defense attorney in "Molly's Game" who helps Molly Bloom navigate her legal troubles with the FBI.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cc50d808190a3c8d1ada31a6a91 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60ab9a9b08190903c1ce6d91af2b5 |
completed | May 2, 2026, 2:31 p.m. |
Created at: April 8, 2026, 9:51 p.m.