Triple
T21045447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Felix M. Warburg House |
E518435
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Museum Mile |
—
|
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: Museum Mile | Statement: [Felix M. Warburg House, partOf, Museum Mile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Museum Mile Context triple: [Felix M. Warburg House, partOf, Museum Mile]
-
A.
Museum Mile
chosen
Museum Mile is a renowned stretch of New York City's Fifth Avenue famous for its dense concentration of major art and cultural museums.
-
B.
Museum Mile
Museum Mile is a prominent cultural district in Bonn, Germany, known for its dense concentration of major museums and art institutions.
-
C.
Museum Row
Museum Row is a prominent stretch of cultural institutions and museums in Los Angeles, known for its dense concentration of major art, history, and science museums.
-
D.
Museum District
The Museum District is a cultural neighborhood in Houston, Texas, known for its high concentration of museums, galleries, and cultural institutions.
-
E.
Museum District
Museum District is a cultural neighborhood in Tacoma, Washington, known for its concentration of major museums and arts institutions.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf3cac081909915a440fbb5c084 |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:32 p.m.