Triple
T9429459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katrina Bowden |
E227334
|
entity |
| Predicate | characterPortrayed |
P1507
|
FINISHED |
| Object | Cerie Xerox |
E227342
|
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: Cerie Xerox | Statement: [Katrina Bowden, characterPortrayed, Cerie Xerox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cerie Xerox Context triple: [Katrina Bowden, characterPortrayed, Cerie Xerox]
-
A.
Cerie Xerox
chosen
Cerie Xerox is a recurring character on the sitcom "30 Rock," known as Liz Lemon’s attractive, laid-back assistant in the TGS writers’ room.
-
B.
Xerox
Xerox is an American corporation best known for pioneering photocopiers and influential computing innovations, including early graphical user interfaces and office software.
-
C.
Raimondi
Raimondi is an Italian surname borne by various notable figures in fields such as science, the arts, and public life.
-
D.
Xerox Network Systems
Xerox Network Systems (XNS) is a pioneering suite of network protocols developed by Xerox in the late 1970s that strongly influenced later networking technologies and protocol stacks.
-
E.
Ricoh
Ricoh is a Japanese multinational imaging and electronics company best known for its cameras, printers, copiers, and office equipment solutions.
- 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_69ca8436ba308190903e470776d2d893 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7c94719c81909d7743a57c45e07f |
completed | April 1, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d11038f7b88190bd6b895f5544c63e |
completed | April 4, 2026, 1:20 p.m. |
Created at: March 30, 2026, 7:49 p.m.