Triple
T7591110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johns |
E179736
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object | John |
unclear NED1
|
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: John | Statement: [Johns, derivedFrom, John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Context triple: [Johns, derivedFrom, John]
-
A.
John
John is the given name of the American composer John Luther Adams, known for his works inspired by nature and environmental themes.
-
B.
John
John is the first name of John Dashwood, a character in Jane Austen's novel "Sense and Sensibility."
-
C.
John
John is the given first name of American character actor and comedian Rags Ragland.
-
D.
John
John is the given name of actor John Cho, a Korean American performer known for roles in the "Harold & Kumar" films and the "Star Trek" reboot series.
-
E.
John
John is the first name of the fictional character John Connor, the prophesied leader of the human resistance in the Terminator franchise.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9b746ac8190b255afdfb9635f72 |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c861673ae48190b86e8023fd02771c |
completed | March 28, 2026, 11:16 p.m. |
Created at: March 27, 2026, 3:53 p.m.