Triple
T29777944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Janus |
E755432
|
entity |
| Predicate | positionOnUnionFees |
P96243
|
FINISHED |
| Object | opposed mandatory agency fees for non‑member public employees |
—
|
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: opposed mandatory agency fees for non‑member public employees | Statement: [Mark Janus, positionOnUnionFees, opposed mandatory agency fees for non‑member public employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionOnUnionFees Context triple: [Mark Janus, positionOnUnionFees, opposed mandatory agency fees for non‑member public employees]
-
A.
positionOnUnion
chosen
Indicates the stance or viewpoint an entity holds regarding a union, such as support, opposition, or neutrality.
-
B.
hasUnionPosition
Indicates that an entity holds or occupies a specific position, role, or office within a labor union or similar union organization.
-
C.
orderInUnion
Indicates the relative position or sequence of an entity within a union or ordered collection of entities.
-
D.
feeUnit
Indicates the unit of measurement or currency in which a fee amount is expressed.
-
E.
effectOnUnion
Indicates the impact or influence that something has on a union as a whole.
- 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_69f0ef878574819088c867fd1a5c8b86 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69fdd2be648c8190b60b3d1caeb44364 |
completed | May 8, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69fdd14a5c708190a6f95ec61f4fc28f |
completed | May 8, 2026, 12:04 p.m. |
Created at: April 28, 2026, 8:48 p.m.