Triple
T22496831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew |
E556162
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Mat |
—
|
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: Mat | Statement: [Matthew, hasShortForm, Mat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mat Context triple: [Matthew, hasShortForm, Mat]
-
A.
Mat
chosen
Mat is a common shortened form of the given name Matthew, often used as an informal or familiar nickname.
-
B.
MAT
MAT is the stock ticker symbol for Mattel, Inc., a major American toy manufacturing and entertainment company known for brands like Barbie and Hot Wheels.
-
C.
MAT
MAT is the commonly used abbreviation for the Moscow Art Theatre, a historic and influential Russian theatre company renowned for its pioneering work in modern drama and acting techniques.
-
D.
MAT
MAT is the National Rail station code for Matlock railway station in Derbyshire, England.
-
E.
Mart
Mart is a prominent modern and contemporary art museum based in Rovereto, Italy, known for its extensive collections and cultural programming.
- 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_69e11e5445bc8190b6a9481926db3355 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15cb2644c819094864bd88bcebcbd |
completed | April 29, 2026, 1:19 a.m. |
Created at: April 16, 2026, 8:50 p.m.