Triple
T17426938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Akalitus |
E423761
|
entity |
| Predicate | hasColleague |
P398
|
FINISHED |
| Object | Thor Lundgren |
—
|
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: Thor Lundgren | Statement: [Gloria Akalitus, hasColleague, Thor Lundgren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thor Lundgren Context triple: [Gloria Akalitus, hasColleague, Thor Lundgren]
-
A.
Thor Lundgren
chosen
Thor Lundgren is a character on the medical drama series "Nurse Jackie," working alongside Jackie Peyton at All Saints Hospital.
-
B.
Bo Lundgren
Bo Lundgren is a Swedish politician and former leader of the Moderate Party who also served as Sweden’s Minister for Fiscal and Financial Affairs.
-
C.
Kim Lundgren
Kim Lundgren is an entrepreneur best known as one of the founders behind the German airline Air Berlin.
-
D.
Dolph Lundgren
Dolph Lundgren is a Swedish actor, director, and martial artist best known for his tough-guy roles in action films such as "Rocky IV" and "The Expendables" series.
-
E.
George Englund
George Englund was an American film editor, director, and producer known for works like "The Ugly American" and for his long marriage to actress Cloris Leachman.
- 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e448fcbf54819091babed0b9b05716 |
completed | April 19, 2026, 3:16 a.m. |
Created at: April 10, 2026, 5:46 a.m.