Triple
T10410770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Guggenheim |
E245381
|
entity |
| Predicate | usedFortuneFor |
P43040
|
FINISHED |
| Object | philanthropy in aviation |
—
|
LITERAL 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: philanthropy in aviation | Statement: [Daniel Guggenheim, usedFortuneFor, philanthropy in aviation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedFortuneFor Context triple: [Daniel Guggenheim, usedFortuneFor, philanthropy in aviation]
-
A.
usedFor
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
B.
usedCapital
Indicates that an entity made use of a particular capital city as its seat of government or primary administrative center.
-
C.
usedFund
chosen
Indicates that one entity expended or applied a particular fund or financial resource for some purpose.
-
D.
usedBoonsFrom
Indicates that one entity has utilized or expended beneficial effects, powers, or advantages that originated from another entity.
-
E.
usedAgainst
Indicates that one entity is employed, applied, or deployed in opposition to, or for the purpose of affecting, another entity.
- F. None of above.
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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9fb98748190a3a6c161edd8f400 |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb6f160819090040644a12395ec |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:09 p.m.