Triple
T10219743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Blitzer |
E242542
|
entity |
| Predicate | owns |
P347
|
FINISHED |
| Object | Brøndby IF |
E548723
|
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: Brøndby IF | Statement: [David Blitzer, owns, Brøndby IF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brøndby IF Context triple: [David Blitzer, owns, Brøndby IF]
-
A.
Brøndby
chosen
Brøndby is a suburban municipality in the western part of the Copenhagen metropolitan area in Denmark, known for its residential districts and the football club Brøndby IF.
-
B.
Randers FC
Randers FC is a professional Danish football club based in the city of Randers that competes in the Danish Superliga.
-
C.
Odense Boldklub
Odense Boldklub is a Danish professional football club based in the city of Odense, known for competing in the top tiers of Danish football.
-
D.
Vejle Boldklub
Vejle Boldklub is a Danish professional football club known for its historic success in the national league and cup competitions.
-
E.
FC Midtjylland
FC Midtjylland is a Danish professional football club known for its data-driven approach to player recruitment and performance, competing in the top tier of Danish football.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa715a3c8190a9ccee7bcece0346 |
completed | April 6, 2026, 12:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a82c98fc8190929b7b56f9a6e60d |
completed | April 8, 2026, 7:10 p.m. |
Created at: April 6, 2026, 11:08 a.m.