Triple
T16404179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ben van Berkel |
E398380
|
entity |
| Predicate | hasOfficeIn |
P1268
|
FINISHED |
| Object | Melbourne |
E4488
|
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: Melbourne | Statement: [Ben van Berkel, hasOfficeIn, Melbourne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melbourne Context triple: [Ben van Berkel, hasOfficeIn, Melbourne]
-
A.
Melbourne
chosen
Melbourne is a major Australian city known for its vibrant arts scene, diverse culture, and status as a leading center for sports and education.
-
B.
Melbourne
Melbourne is a historic market town in Derbyshire, England, known for its Georgian architecture and the notable Melbourne Hall and gardens.
-
C.
Melbourne
Melbourne is a coastal city in east-central Florida known for its beaches, aerospace and technology industries, and role as a commercial and cultural hub of the Space Coast.
-
D.
Melbourn
Melbourn is a village and civil parish in South Cambridgeshire, England, known for its historic architecture and rural community character.
-
E.
Sydney
Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327d1f16481909adb19dab86dcc72 |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00457324948190b803b715cea8b86d |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 5:09 a.m.