Triple
T18688100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mountain Fountain |
E456917
|
entity |
| Predicate | notableWorkOf |
P4
|
FINISHED |
| Object | Bill Culbert |
—
|
NE NERFINISHED |
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: Bill Culbert | Statement: [Mountain Fountain, notableWorkOf, Bill Culbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Culbert Context triple: [Mountain Fountain, notableWorkOf, Bill Culbert]
-
A.
Bill Culbert
chosen
Bill Culbert was a New Zealand-born artist renowned for his innovative use of light and found objects in sculptural and installation works.
-
B.
Jimmy Kirkpatrick
Jimmy Kirkpatrick is a relative of Miguel Tyson, who is known as one of Mike Tyson’s sons.
-
C.
Bill Ferguson
Bill Ferguson is an American Democratic politician who serves as president of the Maryland Senate, representing Baltimore in the state legislature.
-
D.
Mike Donnelly
Mike Donnelly is the well-meaning but accident-prone protagonist of the comedy film "Black Sheep," whose misadventures jeopardize his brother’s political campaign.
-
E.
Brian Berry
Brian Berry is a prominent British geographer known for his influential work in urban and regional analysis and contributions to quantitative geography.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d391eb488190ac2e9abf5bf255e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e55b2e4c6081908bb958bd00d6bec9 |
completed | April 19, 2026, 10:46 p.m. |
Created at: April 10, 2026, 11:49 a.m.