Triple
T9416742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ainsley Whitly |
E227041
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ainsley |
E115096
|
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: Ainsley | Statement: [Ainsley Whitly, givenName, Ainsley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ainsley Context triple: [Ainsley Whitly, givenName, Ainsley]
-
A.
Ainsley
chosen
Ainsley is a secondary character in Margaret Atwood's novel "The Edible Woman," known for her conventional femininity and contrasting attitudes toward gender roles compared to the protagonist.
-
B.
Ashley
Ashley is a character featured in the animated children’s series "¡Dos!"
-
C.
Ashley
Ashley is a given name commonly used in English-speaking countries for both males and females.
-
D.
Ashley
Ashley is a small village and civil parish located within the county of Suffolk in eastern England.
-
E.
Aubrey
Aubrey is a small suburban town in the greater Dallas–Fort Worth metropolitan area in Texas.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd68cb4be08190a47f901a9703f9db |
completed | April 1, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107bfd73481908e07d0ee2774bd59 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:48 p.m.