Triple
T12943765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bigfoot Bjornsen |
E309705
|
entity |
| Predicate | relationshipToDocSportello |
P84787
|
FINISHED |
| Object | professional rival |
—
|
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: professional rival | Statement: [Bigfoot Bjornsen, relationshipToDocSportello, professional rival]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToDocSportello Context triple: [Bigfoot Bjornsen, relationshipToDocSportello, professional rival]
-
A.
relationshipToState
Indicates a relationship or connection that an entity has with a particular state or governmental body.
-
B.
relationshipToARP
Indicates a specified type of relationship or association that an entity has to an ARP (which may represent a particular person, program, plan, or reference point).
-
C.
relationToCouncil
Indicates the nature or type of connection an entity has with a council, such as membership, oversight, affiliation, or governance relationship.
-
D.
subjectRelation
chosen
Indicates that one entity stands in a specified relational role or connection to another entity.
-
E.
reportsRelationship
Indicates that one entity formally provides information, findings, or status about another entity or situation.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:43 p.m.