Triple
T33557690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wren Petersen |
E859525
|
entity |
| Predicate | hasProfessionContext |
P124115
|
FINISHED |
| Object | humanitarian aid |
—
|
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: humanitarian aid | Statement: [Wren Petersen, hasProfessionContext, humanitarian aid]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionContext Context triple: [Wren Petersen, hasProfessionContext, humanitarian aid]
-
A.
hasProfessionSetting
Indicates that an entity’s professional activity or role is carried out within a particular setting or environment.
-
B.
subjectHasOccupationContext
Indicates that a subject’s occupation is specified or interpreted within a particular contextual framework (such as time, place, or situation).
-
C.
hasGivenProfession
chosen
Indicates that an entity holds or practices a specified profession or occupation.
-
D.
hasProfessionInNarrative
Indicates that an entity holds or is assigned a particular profession or occupational role within the context of a narrative or story.
-
E.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
- 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_69f3497b2b68819093207971b5e13dc8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a01a819644c819094c58d083d525da0 |
completed | May 11, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_6a01a5dfd628819083c032d8cfd2e726 |
completed | May 11, 2026, 9:48 a.m. |
Created at: May 1, 2026, 1:40 a.m.