Triple
T7013485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vernor Vinge |
E162641
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | The Cookie Monster |
E290271
|
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: The Cookie Monster | Statement: [Vernor Vinge, notableWork, The Cookie Monster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Cookie Monster Context triple: [Vernor Vinge, notableWork, The Cookie Monster]
-
A.
Cookie Monster
chosen
Cookie Monster is a beloved blue, googly-eyed Muppet best known for his voracious appetite for cookies and his appearances on the children's television show Sesame Street.
-
B.
Oscar the Grouch
Oscar the Grouch is a beloved Sesame Street Muppet known for his grumpy personality, love of trash, and residence in a garbage can.
-
C.
Elmo
Elmo is a deep contextualized word representation model for natural language processing that captures complex characteristics of word use and syntax across different linguistic contexts.
-
D.
Big Bird
Big Bird is a towering yellow bird Muppet from the children's television show "Sesame Street," known for his childlike curiosity and friendly, gentle personality.
-
E.
BigBird
BigBird is a transformer-based language model architecture designed to efficiently handle very long sequences using sparse attention mechanisms.
- 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_69c6885a127c8190867b059bdccf13ff |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc59cbfc8190bba9ebd14143d43c |
completed | March 27, 2026, 7:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a4ff6148190a7a453328507fd6b |
completed | March 28, 2026, 5:42 a.m. |
Created at: March 27, 2026, 2:34 p.m.