Triple
T38298674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Harris |
E1032162
|
entity |
| Predicate | hadInterest |
P152666
|
FINISHED |
| Object | first-person shooter video games |
—
|
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: first-person shooter video games | Statement: [Eric Harris, hadInterest, first-person shooter video games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadInterest Context triple: [Eric Harris, hadInterest, first-person shooter video games]
-
A.
hasHumanInterest
Indicates that something is of particular relevance, appeal, or concern to people, often engaging their emotions, curiosity, or personal experiences.
-
B.
hasVisitorInterest
Indicates that an entity has a particular interest, preference, or attraction toward visiting another entity or location.
-
C.
interestMayBe
chosen
Indicates that one entity potentially has an interest in, or may be interested in, another entity or subject.
-
D.
hasInterestType
Indicates that an entity is associated with a specific category or type of interest it holds or is concerned with.
-
E.
hasPetInterest
Indicates that one entity has an interest in, affinity for, or concern about pets in relation to another entity or context.
- 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_69f76e0f2084819091299d021625c3fe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fccbd826708190b5fab12c4236299a |
completed | May 7, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69fcc58838e08190b8fa54aa5c165f2d |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:30 p.m.