Triple
T14692944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elton Mayo |
E345080
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Elton |
E950289
|
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: Elton | Statement: [Elton Mayo, givenName, Elton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elton Context triple: [Elton Mayo, givenName, Elton]
-
A.
Elton
Elton is the middle name of the influential Canadian science fiction author A. E. van Vogt.
-
B.
Elton
chosen
Elton is a district or locality within the town and parliamentary constituency of Bury North in Greater Manchester, England.
-
C.
Mr. Elton
Mr. Elton is the self-important village vicar and would-be suitor whose vanity and social ambition create romantic complications in the 1996 film adaptation of Jane Austen’s "Emma."
-
D.
Elton John
Elton John is a legendary British singer, pianist, and composer known for his flamboyant style and numerous hit songs across several decades.
-
E.
Englebert
Englebert was a Belgian tyre manufacturer known for supplying racing tyres in early Formula One, including the 1950 World Championship season.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb586e7108190be644db9cf9a4d99 |
completed | April 14, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde18b4bdc8190b5daf05484de7cd8 |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:28 a.m.