Triple
T29138099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Crocker |
E738557
|
entity |
| Predicate | suspectsHasFairies |
P166359
|
FINISHED |
| Object | Timmy Turner |
—
|
NE NERFINISHED |
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: Timmy Turner | Statement: [Mr. Crocker, suspectsHasFairies, Timmy Turner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: suspectsHasFairies Context triple: [Mr. Crocker, suspectsHasFairies, Timmy Turner]
-
A.
suspectsOfCorruptingSister
Indicates that one entity believes or suspects another entity of having corrupted their sister in some way.
-
B.
relativeExecutedForWitchcraft
Indicates that a person has a relative who was executed as punishment for alleged witchcraft.
-
C.
hasFairyCharacters
Indicates that the subject includes or features characters that are fairies.
-
D.
notableSuspect
Indicates that an individual is a particularly significant or prominent suspect in relation to an event, case, or investigation.
-
E.
effectOnSuspects
Indicates how an action, event, or condition influences or impacts the suspects involved.
- 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_69f07cb3adb48190a9e0e169cd026634 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f6626d07e48190903642d4553fdae2 |
completed | May 2, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69f65c2376a08190be5215171e908e69 |
completed | May 2, 2026, 8:18 p.m. |
| PDg | Predicate description generation | batch_69f65f75ac608190a62cd6afce14f68e |
completed | May 2, 2026, 8:32 p.m. |
Created at: April 28, 2026, 11:35 a.m.