Triple
T18136220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joakim Palme |
E434143
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Joakim |
—
|
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: Joakim | Statement: [Joakim Palme, givenName, Joakim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joakim Context triple: [Joakim Palme, givenName, Joakim]
-
A.
Joakim
chosen
Joakim is a masculine given name commonly used in Scandinavian countries, derived from the Hebrew name Joachim.
-
B.
Niklas
Niklas is a masculine given name commonly used in Germanic and Scandinavian countries, derived from the Greek name Nikolaos.
-
C.
Joran
Joran is a given name, likely a regional or linguistic variant of the Scandinavian name Göran.
-
D.
Jakob Jerlström
Jakob Jerlström is a Swedish songwriter and producer known for co-writing international pop hits, including Hailee Steinfeld’s “Most Girls.”
-
E.
Daniel Nannskog
Daniel Nannskog is a retired Swedish striker best known for his prolific goal-scoring spell at Norwegian club Stabæk Fotball and later work as a football pundit.
- 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_69d8b90aac308190801e2c57d8c5bfe5 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4de07b4b4819085fe80beb7addfd0 |
completed | April 19, 2026, 1:52 p.m. |
Created at: April 10, 2026, 10:29 a.m.