Triple
T6000811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edelmann |
E133590
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Edelman |
E24837
|
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: Edelman | Statement: [Edelmann, hasVariant, Edelman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Edelman Context triple: [Edelmann, hasVariant, Edelman]
-
A.
Edelman
chosen
Edelman is a surname most prominently associated with Julian Edelman, a former NFL wide receiver and Super Bowl MVP for the New England Patriots.
-
B.
Edwards
Edwards is a common English surname borne by numerous notable figures across politics, religion, sports, and the arts.
-
C.
Warren
Warren is a common English surname borne by numerous notable figures in politics, law, entertainment, and other fields.
-
D.
Warren
Warren is a small city in northern Pennsylvania known for its historic downtown and location along the Allegheny River.
-
E.
Warren
Warren is a large suburban city in southeast Michigan known for its extensive automotive and defense manufacturing industries.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04ee7c0e08190a6e78969448b070a |
completed | March 22, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1088366f08190bd65374d7a44fbc8 |
completed | March 23, 2026, 9:31 a.m. |
Created at: March 22, 2026, 4:05 p.m.