Triple
T7094245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burlington Industries, Inc. v. Ellerth |
E165279
|
entity |
| Predicate | harassmentType |
P36524
|
FINISHED |
| Object | hostile work environment |
—
|
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: hostile work environment | Statement: [Burlington Industries, Inc. v. Ellerth, harassmentType, hostile work environment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: harassmentType Context triple: [Burlington Industries, Inc. v. Ellerth, harassmentType, hostile work environment]
-
A.
typeOfAbuse
chosen
Indicates the specific kind or category of abusive behavior that one entity inflicts on another.
-
B.
threatType
Indicates the specific category or nature of a threat that one entity poses or represents in relation to another.
-
C.
hasTypeOfViolence
Indicates that an entity involves, exhibits, or is characterized by a specific kind or category of violent behavior or action.
-
D.
infringementType
Indicates the specific category or nature of a violation or breach committed in relation to a rule, right, or law.
-
E.
threatTypeAddressed
Indicates that a given action, measure, or entity is specifically intended to counter or mitigate a particular type of threat.
- 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_69c6887e8c10819091cee237560d32da |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e55159848190a794ad77e60c5525 |
completed | March 27, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c172148190bf290c07bf579d1f |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:41 p.m.