Triple
T6837551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clan Grant |
E157486
|
entity |
| Predicate | hasSept |
P60340
|
FINISHED |
| Object | Grant |
E182443
|
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: Grant | Statement: [Clan Grant, hasSept, Grant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grant Context triple: [Clan Grant, hasSept, Grant]
-
A.
Grant
chosen
Grant is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
-
B.
Grant
Grant is a publishing company best known for releasing special and limited editions of Stephen King’s works, including volumes in The Dark Tower series.
-
C.
Jones
Jones is a common English-language surname borne by numerous notable individuals across fields such as entertainment, sports, politics, and science.
-
D.
Red Grant
Red Grant is a ruthless, psychopathic assassin and primary antagonist in the James Bond franchise, most prominently appearing as SPECTRE’s top killer in the film and novel "From Russia, with Love."
-
E.
Guber
Guber is a surname most prominently associated with American film producer and executive Peter Guber.
- 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_69c6882c53608190b99aebef079b23bd |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d67db4008190b86b497bf6f0c73a |
completed | March 27, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72fb02e0c8190ae1514875e03208a |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:19 p.m.