Triple
T4961270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anneke Wills |
E111412
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Wills |
E126897
|
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: Wills | Statement: [Anneke Wills, familyName, Wills]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wills Context triple: [Anneke Wills, familyName, Wills]
-
A.
Wills
chosen
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.
-
B.
Will
Will is a common shortened form of the given name William, frequently used as a familiar or informal first name.
-
C.
WIL
WIL is the IATA airport code for Wilson Airport, a busy domestic and regional airport serving Nairobi, Kenya.
-
D.
Wil
Wil is a common shortened form of the given name Willem, often used as an informal or familiar nickname.
-
E.
Afterlives
Afterlives is a historical novel by Nobel laureate Abdulrazak Gurnah that follows intertwined lives in German-occupied East Africa and its aftermath, exploring themes of colonialism, memory, and survival.
- 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_69bd4419393c819086319a6fe4bf8542 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd71dc06a48190827d54a5c0351aab |
completed | March 20, 2026, 4:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81e7dba88190ab0f2d99a931cf0e |
completed | March 21, 2026, 11:32 a.m. |
Created at: March 20, 2026, 1:32 p.m.