Triple
T6321932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Bennett (Ted) |
E141761
|
entity |
| Predicate | hasBestFriend |
P27082
|
FINISHED |
| Object | Ted (character) |
E540767
|
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: Ted (character) | Statement: [John Bennett (Ted), hasBestFriend, Ted (character)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ted (character) Context triple: [John Bennett (Ted), hasBestFriend, Ted (character)]
-
A.
Ted
Ted is a masculine given name, often a diminutive of Theodore or Edward, commonly used in English-speaking countries.
-
B.
Ted
Ted is a 2012 comedy film about a foul-mouthed living teddy bear, created by and starring Seth MacFarlane.
-
C.
Ted (living teddy bear)
chosen
Ted (living teddy bear) is a foul-mouthed, magically animated stuffed bear who serves as the crude yet lovable best friend of John Bennett in the comedic Ted film franchise.
-
D.
Teddy
Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
-
E.
Teddy
Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
- 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_69c008d13b8c8190be47d896eb735605 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064c76dfc8190a1d44fd0c4402a0e |
completed | March 22, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d33d70c8190af6acdf5158f9067 |
completed | March 27, 2026, 7:09 a.m. |
Created at: March 22, 2026, 4:29 p.m.