Triple
T9753637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syndrome |
E236500
|
entity |
| Predicate | relationshipToMrIncredible |
P38921
|
FINISHED |
| Object | former fan turned nemesis |
—
|
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: former fan turned nemesis | Statement: [Syndrome, relationshipToMrIncredible, former fan turned nemesis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMrIncredible Context triple: [Syndrome, relationshipToMrIncredible, former fan turned nemesis]
-
A.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
B.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
C.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
D.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
E.
relationshipToDudley
Indicates the specific familial or social relationship that one entity has to the person named Dudley.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9fb01ad08190b2435fa505c622bc |
completed | April 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:24 p.m.