Triple
T8110021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Lee |
E189325
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ann |
E33934
|
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: Ann | Statement: [Ann Lee, givenName, Ann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Context triple: [Ann Lee, givenName, Ann]
-
A.
Ann
chosen
Ann is a given name commonly used as a feminine first or middle name in English-speaking countries.
-
B.
Anna
Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
-
C.
Anna
Anna is a spirited and optimistic princess from Disney's animated film "Frozen," known for her bravery, loyalty, and deep love for her sister Elsa.
-
D.
Anna
Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
-
E.
Anna
Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
- 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_69ca82b9d5848190a24672775d5c5011 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb42fcfb9c81908496f9a7e30d0d8a |
completed | March 31, 2026, 3:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc9421794081909170ea11b09ae357 |
completed | April 1, 2026, 3:42 a.m. |
Created at: March 30, 2026, 5:32 p.m.