Triple
T2256493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Swanson |
E49738
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Swanson |
E107925
|
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: Swanson | Statement: [Robert Swanson, familyName, Swanson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Swanson Context triple: [Robert Swanson, familyName, Swanson]
-
A.
Swanson
chosen
Swanson is a well-known American food brand recognized for its canned broths, stocks, and frozen meals.
-
B.
Carlson
Carlson is a common surname of Scandinavian origin borne by numerous notable individuals across fields such as sports, politics, and entertainment.
-
C.
Cambridge Naturals
Cambridge Naturals is a local health and wellness retailer known for offering natural foods, supplements, and eco-friendly personal care products.
-
D.
Inman
Inman is the introspective Confederate deserter and central protagonist of the film "Cold Mountain," portrayed by Jude Law as he journeys home through the ravages of the American Civil War.
-
E.
Vivanco
Vivanco is a Spanish-language surname of likely Iberian origin borne by various notable individuals.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc1570dc88190bb2b17ed4c25dbb5 |
completed | March 7, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b2229288190a7da9025dc394e67 |
completed | March 9, 2026, 6:39 a.m. |
Created at: March 4, 2026, 7:47 p.m.