Triple
T6656177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edwin Myers Shawn |
E150949
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Shawn |
E289134
|
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: Shawn | Statement: [Edwin Myers Shawn, familyName, Shawn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shawn Context triple: [Edwin Myers Shawn, familyName, Shawn]
-
A.
Shawn
chosen
Shawn is a given name, typically a variant of the name John or Sean, used primarily in English-speaking countries.
-
B.
Shawn Matthews
Shawn Matthews is a senior executive associated with the financial services firm Cantor Fitzgerald.
-
C.
Shaun
Shaun is a given name associated with the English actor Sean Bean, known for his roles in film and television such as "The Lord of the Rings" and "Game of Thrones."
-
D.
Shaun
Shaun is the hapless yet determined protagonist of the British zombie comedy film "Shaun of the Dead," known for his slacker lifestyle and reluctant heroism during a zombie outbreak.
-
E.
Shawn Hatosy
Shawn Hatosy is an American actor known for his roles in films like "Alpha Dog" and the TV series "Animal Kingdom."
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b06dbbf88190b39564a688c25a24 |
completed | March 27, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eefffdfc8190bc191606bdb481a5 |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2:01 p.m.