Triple
T17351486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aunt May |
E421823
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Parker |
E44427
|
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: Parker | Statement: [Aunt May, familyName, Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parker Context triple: [Aunt May, familyName, Parker]
-
A.
Parker
Parker is a 2013 American crime thriller film starring Jason Statham as a professional thief who seeks revenge after being double-crossed by his crew.
-
B.
Parker
Parker is a suburban town in Colorado located along the eastern edge of the Denver metropolitan area.
-
C.
Parker
Parker is a central criminal antihero in the neo-noir crime film "The Way of the Gun," known for his ruthless pragmatism and involvement in a high-stakes kidnapping plot.
-
D.
Parker
Parker is the troubled, tattoo-obsessed protagonist of Flannery O’Connor’s short story “Parker’s Back,” whose spiritual and personal turmoil drive the narrative.
-
E.
Parker
chosen
Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a2ca0708190aae8306ec3a6f2a7 |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0195585e5881909b0ad386b65112ba |
completed | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 5:44 a.m.