Triple
T14961020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katherine Elizabeth Addison |
E373063
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Addison |
E644496
|
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: Addison | Statement: [Katherine Elizabeth Addison, hasFamilyName, Addison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Addison Context triple: [Katherine Elizabeth Addison, hasFamilyName, Addison]
-
A.
Addison
Addison is a small town located in Winston County, Alabama, known for its rural character and tight-knit community.
-
B.
Addison
Addison is a Chicago Transit Authority 'L' station on the Blue Line serving the city's Northwest Side.
-
C.
Addison
Addison is the middle name of Lewis Armistead, a Confederate general best known for his role in Pickett’s Charge during the American Civil War.
-
D.
Addison
chosen
Addison is a common English surname and given name, historically meaning "son of Adam" and borne by various notable figures.
-
E.
Addison
Addison is a small, business-focused town in the Dallas–Fort Worth metropolitan area known for its dense concentration of restaurants, corporate offices, and frequent special events.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cece6881908cd8c8fe41583bee |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8bdd27808190b52bdbf5da5b01d6 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:40 a.m.