Triple
T12671005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freddy |
E302680
|
entity |
| Predicate | relationshipToAudience |
P106211
|
FINISHED |
| Object | mediates surreal events for the audience |
—
|
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: mediates surreal events for the audience | Statement: [Freddy, relationshipToAudience, mediates surreal events for the audience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToAudience Context triple: [Freddy, relationshipToAudience, mediates surreal events for the audience]
-
A.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
B.
relationshipToCustomers
Indicates the type or nature of connection an entity has with its customers, such as role, status, or interaction pattern.
-
C.
relationshipToRelative
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
-
D.
relationshipToEvent
Indicates the specific way an entity is connected or related to a particular event.
-
E.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69d961acadb8819098de743bc951fedb |
completed | April 10, 2026, 8:46 p.m. |
Created at: April 9, 2026, 5:20 p.m.