Triple
T26829372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Otis |
E675455
|
entity |
| Predicate | reactionToBloodstain |
P161522
|
FINISHED |
| Object | treatsAsCleaningProblem |
—
|
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: treatsAsCleaningProblem | Statement: [Mrs. Otis, reactionToBloodstain, treatsAsCleaningProblem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reactionToBloodstain Context triple: [Mrs. Otis, reactionToBloodstain, treatsAsCleaningProblem]
-
A.
bloodStatus
Indicates the classification of an entity based on the type or purity of its blood or lineage.
-
B.
bloodMixedWith
Indicates that the blood of one entity is combined or intermixed with the blood of another entity.
-
C.
bloodStatusTargetOfPrejudice
Indicates that an entity is the target of prejudice or discrimination specifically because of its blood status.
-
D.
obtainsBloodFrom
Indicates that one entity receives or collects blood from another entity.
-
E.
hasBlood
Indicates that one entity possesses or contains the blood of another entity.
- 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_69eee9b776448190993a60b67fcc9545 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61adb3c348190ac6b1744ee520276 |
completed | May 2, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69f611ad2eb48190ac1ed0090f13f7a9 |
completed | May 2, 2026, 3:01 p.m. |
| PDg | Predicate description generation | batch_69f6142a0b988190b404d078f73c3cb9 |
completed | May 2, 2026, 3:11 p.m. |
Created at: April 27, 2026, 5 a.m.