Triple
T27433482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jan Schlichtmann |
E690711
|
entity |
| Predicate | hasGenreOfCase |
P175977
|
FINISHED |
| Object | environmental pollution lawsuit |
—
|
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: environmental pollution lawsuit | Statement: [Jan Schlichtmann, hasGenreOfCase, environmental pollution lawsuit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenreOfCase Context triple: [Jan Schlichtmann, hasGenreOfCase, environmental pollution lawsuit]
-
A.
hasGenreOfClaim
Indicates that a claim is categorized or classified under a particular genre or type of claim.
-
B.
hasGenreFeature
Indicates that something possesses a characteristic, element, or trait associated with a particular genre.
-
C.
hasGivenGenre
Indicates that an entity is associated with a specific genre that has been assigned or designated to it.
-
D.
hasUseGenre
Indicates that something (such as a work, product, or item) is associated with or categorized under a particular genre for its use or purpose.
-
E.
hasGenreRelation
Indicates that there is an association between an entity and a specific genre, specifying the type or category it belongs to.
- F. None of above. chosen
Provenance (4 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_69ef5200fa0481908e28508d6e2c149e |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f6db3606808190a80c6e9f5da5b33e |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
| PDg | Predicate description generation | batch_69f6db1bc348819097c844f76e2fa4fe |
completed | May 3, 2026, 5:20 a.m. |
Created at: April 27, 2026, 12:43 p.m.