Triple
T233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carnegie Institution of Washington |
E4
|
entity |
| Predicate | legalForm |
P64
|
FINISHED |
| Object | nonprofit organization |
—
|
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: nonprofit organization | Statement: [Carnegie Institution of Washington, legalForm, nonprofit organization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalForm Context triple: [Carnegie Institution of Washington, legalForm, nonprofit organization]
-
A.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
B.
languageOfWorkOrName
Indicates the language in which a work is created or a name is expressed.
-
C.
fullName
Indicates that an entity has a complete personal name, typically combining given name(s) and family name into a single string.
-
D.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
E.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
- F. None of above. chosen
Provenance (4 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_69a222a954e48190b48f126a67485661 |
completed | Feb. 27, 2026, 11:03 p.m. |
| NER | Named-entity recognition | batch_69a2266edf048190828e8f53cb7f6ba6 |
completed | Feb. 27, 2026, 11:19 p.m. |
| PD | Predicate disambiguation | batch_69a222f9916081908db2eedc81d85301 |
completed | Feb. 27, 2026, 11:04 p.m. |
| PDg | Predicate description generation | batch_69a2266e0fb4819081d1775e498ed96a |
completed | Feb. 27, 2026, 11:19 p.m. |
Created at: Feb. 27, 2026, 11:04 p.m.