Triple
T17609254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jared Huffman |
E428924
|
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: [Jared Huffman, familyName, Huffman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huffman Context triple: [Jared 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.
Compressor
Compressor is Apple's professional video and audio encoding and transcoding application used to create optimized media outputs for various formats and platforms.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4e6ba48190804e113983e7c704 |
completed | April 19, 2026, 5:46 a.m. |
Created at: April 10, 2026, 5:51 a.m.