Triple
T1188194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Byron Nelson |
E25295
|
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: [Byron Nelson, familyName, Nelson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nelson Context triple: [Byron Nelson, familyName, Nelson]
-
A.
Nelson
chosen
Nelson is a common English-language surname borne by numerous notable figures across politics, sports, entertainment, and academia.
-
B.
Nelson
Nelson is a former mill town in Lancashire, England, known for its industrial heritage and location near the Pennine hills.
-
C.
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.
-
D.
Gilbert
Gilbert is a rapidly growing suburban town in the southeastern Phoenix metropolitan area known for its family-friendly communities and high quality of life.
-
E.
Gilbert
Gilbert is a masculine given name of Norman-French origin that has been borne by various notable figures, including philosophers, writers, and entertainers.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd568cf481908d10cf19a3ce28f3 |
completed | March 1, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8a0646388190b440451d786db04c |
completed | March 7, 2026, 8:26 p.m. |
Created at: March 1, 2026, 7:45 p.m.