Triple
T9266975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympic Games mascots |
E222724
|
entity |
| Predicate | notableExample |
P1503
|
FINISHED |
| Object | Izzy |
E88018
|
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: Izzy | Statement: [Olympic Games mascots, notableExample, Izzy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Izzy Context triple: [Olympic Games mascots, notableExample, Izzy]
-
A.
Izzy
chosen
Izzy was the abstract, computer-generated mascot character created for the 1996 Summer Olympics in Atlanta.
-
B.
Izzy
Izzy is a person or character known primarily in relation to someone named Cobi, coming after them in a sequence or grouping.
-
C.
Izzy the Islander
Izzy the Islander is the costumed mascot representing the Texas A&M University–Corpus Christi Islanders athletic teams and campus spirit.
-
D.
Izzy Baline
Izzy Baline is the birth name of Irving Berlin, the famed American composer and lyricist behind classics like "White Christmas" and "God Bless America."
-
E.
Pippy
Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
- 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_69ca841f2e808190a64f4c31903a1332 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd074bac9481909419988a9e8d9bd5 |
completed | April 1, 2026, 11:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09c193b548190afe79d0c84fa2bd3 |
completed | April 4, 2026, 5:05 a.m. |
Created at: March 30, 2026, 7:33 p.m.