Triple
T14766396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mats |
E347006
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Matts |
E347006
|
NE FINISHED |
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: Matts | Statement: [Mats, hasVariant, Matts]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matts Context triple: [Mats, hasVariant, Matts]
-
A.
Matty
Matty is the famous nickname of Christy Mathewson, one of early baseball’s greatest pitchers and a Hall of Famer for the New York Giants.
-
B.
Matty
Matty is a common diminutive or nickname for the given name Matthew.
-
C.
Mattsies
Mattsies is a village in Bavaria, Germany, known as the home base of the aircraft manufacturer Grob Aircraft AG.
-
D.
Mats
chosen
Mats is a masculine given name commonly used in Scandinavian countries, particularly Sweden and Norway.
-
E.
Matt
Matt is a fictional character from the dark comedy film "The Opposite of Sex," which follows the chaotic fallout of a manipulative teenager’s impact on the lives of those around her.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f576c881909da70627f5897c94 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cf68d94819093567bc630f67b60 |
completed | May 8, 2026, 4:19 p.m. |
Created at: April 10, 2026, 1:30 a.m.