Triple
T10458705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerami Grant |
E246612
|
entity |
| Predicate | father |
P120
|
FINISHED |
| Object | Harvey Grant |
E246611
|
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: Harvey Grant | Statement: [Jerami Grant, father, Harvey Grant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harvey Grant Context triple: [Jerami Grant, father, Harvey Grant]
-
A.
Harvey Grant
chosen
Harvey Grant is a former American professional basketball player who played primarily as a forward in the NBA during the late 1980s and 1990s.
-
B.
Harvey White
Harvey White is an American engineer and entrepreneur best known as a co-founder and early leader of the telecommunications technology company Qualcomm.
-
C.
Harvey Ackroyd
Harvey Ackroyd was an architect known for his work on the Tennessee State Capitol.
-
D.
Charlie Haggers
Charlie Haggers is a recurring character on the satirical 1970s television soap opera "Mary Hartman, Mary Hartman," known for his involvement in the show's darkly comedic small-town dramas.
-
E.
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.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fe4b6d408190af59104a44871578 |
completed | April 7, 2026, 12:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90dac584081909a79bc300b9338c8 |
completed | April 10, 2026, 2:48 p.m. |
Created at: April 6, 2026, 12:18 p.m.