Triple
T20013157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noble Willingham |
E494640
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Noble |
—
|
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: Noble | Statement: [Noble Willingham, givenName, Noble]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noble Context triple: [Noble Willingham, givenName, Noble]
-
A.
Noble
chosen
Noble is a surname of English origin historically associated with social rank and often borne by families of distinction.
-
B.
Noble
Noble is a small city in Cleveland County, Oklahoma, known for its close-knit community and proximity to the Oklahoma City metropolitan area.
-
C.
Noble
Noble is an unincorporated community and residential area within Abington Township in Montgomery County, Pennsylvania.
-
D.
Regal
Regal is a major American movie theater chain known for operating multiplex cinemas across the United States.
-
E.
Regal
Regal is a character featured in the puzzle-adventure video game "Room 25."
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66238f434819083b11458179bb601 |
completed | April 20, 2026, 5:28 p.m. |
Created at: April 11, 2026, 3:34 p.m.