Triple
T34786413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Day |
E1002821
|
entity |
| Predicate | usedByPeopleInField |
P192941
|
FINISHED |
| Object | sports |
—
|
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: sports | Statement: [Michael Day, usedByPeopleInField, sports]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedByPeopleInField Context triple: [Michael Day, usedByPeopleInField, sports]
-
A.
situatedInField
Indicates that an entity is located within or occupies a particular field or area of activity.
-
B.
usedAgriculture
Indicates that an entity employed agricultural methods, practices, or resources for cultivation, production, or related purposes.
-
C.
agricultureUse
Indicates that something is used for, involved in, or designated for agricultural activities or purposes.
-
D.
wasUsedOnCrop
Indicates that something (such as a substance, tool, or method) was applied or utilized on a particular crop.
-
E.
fieldOfUser
Indicates that a user is associated with or specializes in a particular field, domain, or area of expertise.
- 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_69f76db47d408190a24fc7164439ea2d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fd32848ea88190a71e6df402bbb30e |
completed | May 8, 2026, 12:47 a.m. |
| PD | Predicate disambiguation | batch_69fd2d7e95588190991d5f21e25155df |
completed | May 8, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69fd328298ac8190b6bd5ded7dca270d |
completed | May 8, 2026, 12:46 a.m. |
Created at: May 3, 2026, 3:59 p.m.