Triple
T5829644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Santos |
E129312
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Matt |
E354152
|
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: Matt | Statement: [Matt Santos, givenName, Matt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Context triple: [Matt Santos, givenName, Matt]
-
A.
Matt
Matt is the given name of Matt Eberflus, an American football coach best known as the head coach of the Chicago Bears in the NFL.
-
B.
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.
-
C.
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.
-
D.
Matty
chosen
Matty is a common diminutive or nickname for the given name Matthew.
-
E.
Mark
Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
- 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_69c00849d55481908b4f9f5543e0bf6d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0346905848190a8f541401da05604 |
completed | March 22, 2026, 6:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c09866fd248190b4248a993051b732 |
completed | March 23, 2026, 1:33 a.m. |
Created at: March 22, 2026, 3:54 p.m.