Triple
T20771513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katy Brand |
E511247
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Katy |
—
|
NE NERFINISHED |
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: Katy | Statement: [Katy Brand, givenName, Katy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katy Context triple: [Katy Brand, givenName, Katy]
-
A.
Katy
Katy is a tough, sharp-witted woman from the Canadian comedy series "Letterkenny," known for being Wayne’s sister and a core member of the show’s central friend group.
-
B.
Katy
Katy is the popular nickname for the Missouri–Kansas–Texas Railroad, a historic American railway that served the central and southern United States.
-
C.
Katy
chosen
Katy is a common feminine given name, typically used as a diminutive form of Katherine or similar names.
-
D.
Wimberley
Wimberley is a small, scenic town in central Texas known for its picturesque Hill Country landscapes, swimming holes, and artsy, tourist-friendly downtown.
-
E.
Celina
Celina is a rapidly growing suburban city in the northern part of the Dallas–Fort Worth metropolitan area in Texas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ca01148190ac018e57e0cab46f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c266f4b48190891c0db3e322bb23 |
completed | April 21, 2026, 12:18 a.m. |
Created at: April 16, 2026, 12:36 p.m.