Triple
T15442745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weeb Ewbank |
E369945
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Weeb |
E369944
|
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: Weeb | Statement: [Weeb Ewbank, givenName, Weeb]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weeb Context triple: [Weeb Ewbank, givenName, Weeb]
-
A.
Weeb
chosen
Weeb is a casual nickname for Weeb Ewbank, the Hall of Fame American football coach best known for leading the Baltimore Colts and New York Jets to NFL championships.
-
B.
Weebo
Weebo is the quirky, floating robot assistant from Disney’s 1997 film "Flubber," known for its expressive personality and multimedia projections.
-
C.
Dweebs
Dweebs is a short-lived 1990s American sitcom about a group of socially awkward computer geeks working at a tech company.
-
D.
Miquela
Miquela is a feminine given name, commonly used in Spanish- and Portuguese-speaking cultures as a variant of Michaela or Michelle.
-
E.
Shimushiru
Shimushiru is the former Japanese name for Simushir, an uninhabited volcanic island in the central Kuril Islands chain in the northwest Pacific Ocean.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ef55f5c8190a32b1b6ad1daf454 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2cf7ce7c8190810ef35b6e37254d |
completed | May 9, 2026, 12:47 p.m. |
Created at: April 10, 2026, 3:21 a.m.