Triple
T406257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nelson Mandela |
E9390
|
entity |
| Predicate | givenName |
P17
|
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: [Nelson Mandela, givenName, Nelson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nelson Context triple: [Nelson Mandela, givenName, 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.
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.
-
D.
FitzRoy
FitzRoy is a prominent English aristocratic family name historically associated with illegitimate royal descent and borne by several notable dukes and politicians.
-
E.
Fletcher
Fletcher is a surname of English origin borne by numerous notable individuals across fields such as the military, politics, arts, and sports.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ecbc00508190bbb602179273f29c |
completed | Feb. 28, 2026, 1:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4239ffb5c819091b96dbe38a06d07 |
completed | March 1, 2026, 11:31 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.