Triple
T103005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Research Triangle |
E2080
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Garner |
E5231
|
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: Garner | Statement: [Research Triangle, hasPart, Garner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garner Context triple: [Research Triangle, hasPart, Garner]
-
A.
Garner
chosen
Garner is a surname most notably associated with John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
B.
Graham
Graham is the surname of Elizabeth Arden, the pioneering Canadian-American businesswoman who founded the iconic Elizabeth Arden cosmetics empire.
-
C.
Neal
Neal is a masculine given name of Gaelic origin, commonly used in English-speaking countries.
-
D.
Maynard
Maynard is the middle name of the influential British economist John Maynard Keynes, a key figure in modern macroeconomic theory.
-
E.
Gavin
Gavin is a masculine given name of Celtic origin, commonly used in 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a256a9bd5881909f7d87fe65e79b4c |
completed | Feb. 28, 2026, 2:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2fd00c6b88190b5fc1180632cdb25 |
completed | Feb. 28, 2026, 2:34 p.m. |
Created at: Feb. 28, 2026, 2:12 a.m.