Triple
T9480387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonard Nelson |
E228620
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Nelson |
E1145
|
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: Nelson | Statement: [Leonard Nelson, familyName, Nelson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nelson Context triple: [Leonard Nelson, familyName, Nelson]
-
A.
Nelson
Nelson is a former mill town in Lancashire, England, known for its industrial heritage and location near the Pennine hills.
-
B.
Nelson
Nelson is a coastal city at the top of New Zealand’s South Island, known for its arts scene, sunny climate, and access to nearby national parks.
-
C.
Nelson
Nelson is a masculine given name of English origin that has been borne by various notable figures in politics, sports, and the arts.
-
D.
Nelson
chosen
Nelson is a common English-language surname borne by numerous notable figures across politics, sports, entertainment, and academia.
-
E.
Jervis
Jervis is a masculine given name of English origin, historically used in various English-speaking countries.
- 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_69ca84730a5081908de282651019bf2f |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd8016813881908dafc026779c89c4 |
completed | April 1, 2026, 8:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cf7526c8190b96693bdc13743d5 |
completed | April 4, 2026, 3:23 p.m. |
Created at: March 30, 2026, 7:54 p.m.