Triple
T4716691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John William Ferrell |
E104657
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Will |
E3364
|
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: Will | Statement: [John William Ferrell, hasGivenName, Will]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Will Context triple: [John William Ferrell, hasGivenName, Will]
-
A.
Will
chosen
Will is a common shortened form of the given name William, frequently used as a familiar or informal first name.
-
B.
Wills
Wills is a surname most notably associated with Childe Harold Wills, an early automotive engineer and key collaborator of Henry Ford in the development of the Model T.
-
C.
Wil
Wil is a common shortened form of the given name Willem, often used as an informal or familiar nickname.
-
D.
WIL
WIL is the IATA airport code for Wilson Airport, a busy domestic and regional airport serving Nairobi, Kenya.
-
E.
Willing
Willing is a surname most notably associated with American socialite Ava Lowle Willing, who was part of prominent transatlantic high society in the late 19th and early 20th centuries.
- 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd640a32ec8190850146957885c3cf |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be1088103c819098296ce700697e90 |
completed | March 21, 2026, 3:29 a.m. |
Created at: March 20, 2026, 1:18 p.m.