Triple
T75425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roma |
E1507
|
entity |
| Predicate | facesIssue |
P3326
|
FINISHED |
| Object | social exclusion |
—
|
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: social exclusion | Statement: [Roma, facesIssue, social exclusion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facesIssue Context triple: [Roma, facesIssue, social exclusion]
-
A.
facesChallenge
chosen
Indicates that an entity is confronted with a difficulty, obstacle, or demanding situation that must be dealt with or overcome.
-
B.
eagleFaces
Indicates that an eagle is oriented toward, looking at, or facing another entity or direction.
-
C.
issues
Indicates that an entity formally produces, releases, or distributes something, such as a document, order, or resource, making it officially available.
-
D.
usedOwnFaceAsModelFor
Indicates that an entity created or designed something using their own face as the reference or template.
-
E.
frontType
Indicates the type or category of a front (e.g., boundary or leading side) that one entity presents or forms relative to 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25314bd6c81908d1cfd4b83f20049 |
completed | Feb. 28, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_69a24eae77ec81909015906f31f2b62e |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.