Triple
T26016305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LGBTQ+ organizations |
E647031
|
entity |
| Predicate | oftenStructure |
P124802
|
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: [LGBTQ+ organizations, oftenStructure, nonprofit organization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenStructure Context triple: [LGBTQ+ organizations, oftenStructure, nonprofit organization]
-
A.
oftenFrom
Indicates that something frequently originates, derives, or comes from a particular source or location.
-
B.
oftenUse
Indicates that one entity frequently or regularly uses, employs, or utilizes another entity.
-
C.
oftenSetIn
Indicates that something, such as a story or event, frequently takes place within a particular setting or context.
-
D.
oftenHave
chosen
Indicates that one entity frequently possesses, experiences, or is associated with another entity.
-
E.
oftenStatedWith
Indicates that one statement, fact, or expression is frequently mentioned or asserted together with another.
- 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_69e77e8aa65881909ca58918f29ab2a0 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6640168948190811bd5f933a87cf5 |
completed | May 2, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69f6633451948190bcc0410602bb4914 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 22, 2026, 9:03 a.m.