Triple
T16357767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lena Ellison |
E397228
|
entity |
| Predicate | isAssociatedWith |
P2830
|
FINISHED |
| Object | Ellison (surname) |
E87230
|
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: Ellison (surname) | Statement: [Lena Ellison, isAssociatedWith, Ellison (surname)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ellison (surname) Context triple: [Lena Ellison, isAssociatedWith, Ellison (surname)]
-
A.
Ellison
chosen
Ellison is a surname most prominently associated with Larry Ellison, the billionaire co-founder of Oracle Corporation.
-
B.
Lincoln (surname)
Lincoln is an English surname of Old English origin, most famously associated with U.S. President Abraham Lincoln and the historic city of Lincoln in England.
-
C.
Madison (surname)
Madison is an English-language surname that historically originated as a patronymic meaning “son of Maud” or “son of Matthew.”
-
D.
Brown (surname)
Brown is a common English-language surname of British origin, typically derived from a nickname referring to hair color, complexion, or clothing.
-
E.
Howard (surname)
Howard is an English-language surname of Anglo-Norman and sometimes Irish origin, borne by numerous notable figures in politics, entertainment, and other fields.
- 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_69d87f2778dc8190aa95c7572db127e6 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2fad0b1948190a66ec7d2a310cc83 |
completed | April 18, 2026, 3:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a002dbaacd88190b2c90c5be2832307 |
completed | May 10, 2026, 7:03 a.m. |
Created at: April 10, 2026, 5:07 a.m.