Triple
T6043967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyle Budwell |
E134618
|
entity |
| Predicate | relationshipToLeeGates |
P68364
|
FINISHED |
| Object | angry viewer |
—
|
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: angry viewer | Statement: [Kyle Budwell, relationshipToLeeGates, angry viewer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLeeGates Context triple: [Kyle Budwell, relationshipToLeeGates, angry viewer]
-
A.
relationshipToLarryKeller
Indicates the specific type of relationship or connection an entity has to Larry Keller.
-
B.
relationshipToJoeKeller
Indicates the specific familial, social, or personal connection that one entity has to Joe Keller.
-
C.
relationshipToKateKeller
Indicates the specific familial, social, or interpersonal connection that one entity has to Kate Keller.
-
D.
relationshipToAnnDeever
Indicates the specific interpersonal or familial relationship that an entity has to Ann Deever.
-
E.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e2b1148190908c4dc43abee266 |
completed | March 22, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69c049eb52a08190ac10fd703735f5aa |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8d4a148190bd8f95caae978e1b |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:08 p.m.