Triple
T4824769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galen Black |
E107795
|
entity |
| Predicate | soughtBenefit |
P34994
|
FINISHED |
| Object | unemployment compensation |
—
|
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: unemployment compensation | Statement: [Galen Black, soughtBenefit, unemployment compensation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: soughtBenefit Context triple: [Galen Black, soughtBenefit, unemployment compensation]
-
A.
believedBenefit
Indicates that one entity considers or perceives another entity, action, or state as providing an advantage or positive outcome.
-
B.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
C.
soughtTo
chosen
Indicates that one entity attempted or intended to obtain, achieve, or bring about another entity or outcome.
-
D.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
E.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1fe130819087ae01309f96a0c8 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:24 p.m.