Triple
T36312500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | /fit/ |
E894099
|
entity |
| Predicate | hasPrimaryUserInterest |
P25176
|
FINISHED |
| Object | improving physical appearance |
—
|
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: improving physical appearance | Statement: [/fit/, hasPrimaryUserInterest, improving physical appearance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryUserInterest Context triple: [/fit/, hasPrimaryUserInterest, improving physical appearance]
-
A.
hasHumanInterest
Indicates that something is of particular relevance, appeal, or concern to people, often engaging their emotions, curiosity, or personal experiences.
-
B.
hasInterestType
Indicates that an entity is associated with a specific category or type of interest it holds or is concerned with.
-
C.
hasVisitorInterest
Indicates that an entity has a particular interest, preference, or attraction toward visiting another entity or location.
-
D.
hasAreaOfInterest
chosen
Indicates that an entity possesses or is associated with a particular area of interest or focus.
-
E.
hasPolicyInterestOf
Indicates that one entity holds or is associated with a specific policy interest belonging to or concerning another entity.
- 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_69f76e4c1b248190b10667d0213537fe |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:09 p.m.