Triple
T16034996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slim |
E388948
|
entity |
| Predicate | respectedBy |
P8505
|
FINISHED |
| Object | Carlson |
E58048
|
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: Carlson | Statement: [Slim, respectedBy, Carlson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carlson Context triple: [Slim, respectedBy, Carlson]
-
A.
Carlson
chosen
Carlson is a common surname of Scandinavian origin borne by numerous notable individuals across fields such as sports, politics, and entertainment.
-
B.
Korgen
Korgen is a village in Nordland county, Norway, known as the main local hub for services and administration in the municipality of Hemnes.
-
C.
Carls
Carls is a German surname most notably associated with Rolf Carls, a high-ranking naval officer in the Kriegsmarine during World War II.
-
D.
Driscoll
Driscoll is a surname most notably associated with American child actor Bobby Driscoll, known for his work with Disney in the mid-20th century.
-
E.
Carlson Companies
Carlson Companies is a privately held American conglomerate best known for its global presence in the travel, hospitality, and corporate services industries.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1833b84608190887dafda5d081dc0 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbd1cafc81909125174eed475d55 |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 4:56 a.m.