Triple
T10494360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jackie |
E247496
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Jackie |
E87592
|
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: Jackie | Statement: [Jackie, title, Jackie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jackie Context triple: [Jackie, title, Jackie]
-
A.
Jackie
chosen
Jackie is a given name, often used as a diminutive or variant of names like Jack, Jacqueline, or John.
-
B.
Jackie
Jackie is a biographical drama film in which Natalie Portman portrays Jacqueline Kennedy in the aftermath of President John F. Kennedy’s assassination.
-
C.
Jackie Price
Jackie Price is a fictional character from the psychological thriller film "The Jacket."
-
D.
Jackie Peyton
Jackie Peyton is the troubled, painkiller-addicted emergency room nurse at the center of the television series "Nurse Jackie."
-
E.
Jackie Morrow
Jackie Morrow is an actor known for appearing in the Hardy family film "Out West with the Hardys."
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5097fe2bc81909d66ce43f3533284 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d933d4b63081909ad297038fb74bed |
completed | April 10, 2026, 5:31 p.m. |
Created at: April 6, 2026, 12:24 p.m.