Triple
T19968548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cady Huffman |
E480007
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Huffman |
—
|
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: Huffman | Statement: [Cady Huffman, familyName, Huffman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huffman Context triple: [Cady Huffman, familyName, Huffman]
-
A.
Huffman
chosen
Huffman is a surname most commonly associated with the American computer scientist David A. Huffman, known for developing Huffman coding in information theory and data compression.
-
B.
Lloyd’s algorithm
Lloyd’s algorithm is an iterative clustering method that partitions data into k groups by repeatedly assigning points to the nearest cluster center and updating those centers to minimize within-cluster variance.
-
C.
LZH
LZH is the IATA airport code for Liuzhou Bailian Airport, a commercial airport serving Liuzhou in Guangxi, China.
-
D.
LZ77
LZ77 is a foundational lossless data compression algorithm that uses a sliding window to replace repeated occurrences of data with references to a single copy.
-
E.
Brotli
Brotli is a modern, general-purpose lossless compression algorithm developed by Google, known for achieving high compression ratios and efficient web content delivery.
- 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.