Triple
T19968547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cady Huffman |
E480007
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Cady |
—
|
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: Cady | Statement: [Cady Huffman, givenName, Cady]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cady Context triple: [Cady Huffman, givenName, Cady]
-
A.
Cady
chosen
Cady is a surname of English origin borne by various notable individuals, including American jurist Daniel Cady.
-
B.
Cady
Cady is a river in the Conflent region of southern France, known for flowing through the eastern Pyrenees before joining the Têt River.
-
C.
Cady Wells
Cady Wells was an American modernist painter known for his expressive Southwestern landscapes and association with the Taos art colony in New Mexico.
-
D.
Cady Heron
Cady Heron is the naive, homeschooled teenager who becomes entangled in high school cliques and social politics in the teen comedy film "Mean Girls."
-
E.
Cady Short-Thompson
Cady Short-Thompson is an American academic leader and administrator who serves as president of Northern Kentucky University.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc6b0208190b1ae30be95712326 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.