Triple
T7656486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santita Jackson |
E173397
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Jackson |
E18159
|
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: Jackson | Statement: [Santita Jackson, familyName, Jackson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jackson Context triple: [Santita Jackson, familyName, Jackson]
-
A.
Jackson
chosen
Jackson is a common English surname borne by numerous notable figures across politics, science, sports, and the arts.
-
B.
Jackson
Jackson is a major Chicago 'L' station in the Loop that serves the CTA Red Line and connects with multiple other transit lines.
-
C.
Jackson
Jackson is the capital and largest city of Mississippi, known as a regional center for government, education, and culture in the American South.
-
D.
Jackson
Jackson is a city in Michigan that serves as the home of Cascades Falls Park, a popular local landmark known for its illuminated, man-made waterfalls.
-
E.
Jackson
Jackson is the naive but determined protagonist of Chester Himes’s crime novel *A Rage in Harlem*, whose misadventures drive the story’s darkly comic plot.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7018fcbb48190a479f2effd939a8e |
completed | March 27, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b05846c8190b49540aeae43dd9a |
completed | March 29, 2026, 3:22 a.m. |
Created at: March 27, 2026, 3:59 p.m.