Triple
T11435162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nella Larsen |
E270985
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Larsen |
E272819
|
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: Larsen | Statement: [Nella Larsen, familyName, Larsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larsen Context triple: [Nella Larsen, familyName, Larsen]
-
A.
Larsen
chosen
Larsen is a surname of Scandinavian origin borne by numerous notable individuals across fields such as literature, music, and sports.
-
B.
Nellallitea Larsen
Nellallitea Larsen was an American novelist and key figure of the Harlem Renaissance, best known for her works exploring race, identity, and gender such as "Passing" and "Quicksand."
-
C.
Lindberg
Lindberg is a small municipality in the Regen district of Bavaria, Germany, known for its location in the Bavarian Forest region.
-
D.
Thwaites
Thwaites is a surname most prominently associated with Australian actor Brenton Thwaites, known for his roles in film and television.
-
E.
Ulstein
Ulstein is a coastal municipality in western Norway known for its maritime industry and shipbuilding.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c485f481909dd3d9b0993f3faf |
completed | April 9, 2026, 8:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e8ed579c8190b1ddc1dce20d9617 |
completed | April 20, 2026, 8:50 a.m. |
Created at: April 8, 2026, 9:35 p.m.