Triple
T13645364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Bunsen Honeydew |
E326087
|
entity |
| Predicate | hasAssistant |
P30538
|
FINISHED |
| Object | Beaker |
E326088
|
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: Beaker | Statement: [Dr. Bunsen Honeydew, hasAssistant, Beaker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beaker Context triple: [Dr. Bunsen Honeydew, hasAssistant, Beaker]
-
A.
Beaker
chosen
Beaker is a high-strung, squeaky-voiced lab assistant from The Muppets, known for his nervous demeanor and frequent mishaps in scientific experiments.
-
B.
Tumbler
Tumbler is a close associate and ally of master car thief Memphis Raines in the action film "Gone in 60 Seconds."
-
C.
Jug
"Jug" is the widely used nickname for the Republic P-47 Thunderbolt, a rugged and heavily armed American World War II fighter-bomber aircraft.
-
D.
the Teapot
The Teapot is a prominent asterism in the constellation Sagittarius whose stars outline the shape of a traditional teapot in the night sky.
-
E.
Ma Kettle
Ma Kettle is a comically rustic, good-natured farm wife character from the popular mid-20th-century "Ma and Pa Kettle" film 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60635d08190899806fe8936f02a |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78af948408190bca7f2e46863391e |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:51 p.m.