Triple
T19984337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ron Weasley |
E493897
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Ron |
—
|
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: Ron | Statement: [Ron Weasley, nickname, Ron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ron Context triple: [Ron Weasley, nickname, Ron]
-
A.
Ron
chosen
Ron is a central character in the Harry Potter series, known as Harry Potter’s loyal best friend and a member of the Weasley family.
-
B.
Ron
Ron is a malfunctioning but endearing robot companion who forms an unlikely friendship with a socially awkward boy in the animated film "Ron's Gone Wrong."
-
C.
Ron
Ron is a person associated with Brewis, likely as a collaborator, colleague, or acquaintance.
-
D.
Ron
Ron Leibman was an American actor known for his work in film, television, and theater, including his Emmy-winning role in the series "Kaz" and his recurring role as Dr. Leonard Green on "Friends."
-
E.
Ron
Ron is the commonly used first name of American politician Ron DeSantis, the governor of Florida and a prominent figure in contemporary U.S. conservative politics.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d157d088190af861608936e59b7 |
completed | April 20, 2026, 5:06 p.m. |
Created at: April 11, 2026, 3:28 p.m.