Triple
T33103535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelbyville |
E847122
|
entity |
| Predicate | hasNotableObjectInFiction |
P203433
|
FINISHED |
| Object | Shelbyville lemon tree |
—
|
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: Shelbyville lemon tree | Statement: [Shelbyville, hasNotableObjectInFiction, Shelbyville lemon tree]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableObjectInFiction Context triple: [Shelbyville, hasNotableObjectInFiction, Shelbyville lemon tree]
-
A.
hasEventInFiction
Indicates that a fictional work includes or depicts a particular event within its narrative.
-
B.
hasPlaceInFiction
Indicates that a fictional work or element is associated with, set in, or takes place within a particular fictional location or setting.
-
C.
hasFictionalDocument
Indicates that one entity possesses, is associated with, or includes a document that is fictional or exists only within an imagined or narrative context.
-
D.
hasViewOfFictional
Indicates that one entity has a visual or conceptual perspective of a fictional entity or scene.
-
E.
hasNotableFictionalBearer
Indicates that an entity is associated with at least one well-known fictional character that bears its name or designation.
- 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_69f3495686508190b76bf20fa5e00bf7 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a017d27e184819094638c3cf6876de4 |
completed | May 11, 2026, 6:54 a.m. |
| PD | Predicate disambiguation | batch_6a017c785a44819083111384b55769e9 |
completed | May 11, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_6a017d270e9881909e70280ff4e45e9a |
completed | May 11, 2026, 6:54 a.m. |
Created at: May 1, 2026, 1:26 a.m.