Triple
T14710055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grover |
E345524
|
entity |
| Predicate | friendOf |
P8712
|
FINISHED |
| Object | Elmo |
E669491
|
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: Elmo | Statement: [Grover, friendOf, Elmo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elmo Context triple: [Grover, friendOf, Elmo]
-
A.
Elmo
chosen
Elmo is a popular red Muppet character from the children's television show "Sesame Street," known for his cheerful personality and distinctive high-pitched voice.
-
B.
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.
-
C.
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.
-
D.
Big Bird
Big Bird is the nickname for Terminal 1 at Tokyo’s Haneda Airport, a major domestic flight hub known for its extensive shopping and dining facilities.
-
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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb9814e0c8190984ac30d276499cc |
completed | April 14, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf08d59b48190a1ddd2aed6ed756e |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:28 a.m.