Triple
T23053631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winfred-Louder department store |
E574092
|
entity |
| Predicate | hasFictionalCityContext |
P150796
|
FINISHED |
| Object | Cleveland |
—
|
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: Cleveland | Statement: [Winfred-Louder department store, hasFictionalCityContext, Cleveland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalCityContext Context triple: [Winfred-Louder department store, hasFictionalCityContext, Cleveland]
-
A.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
B.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
C.
partOfFictionalCity
Indicates that one entity is a component, area, or subdivision within a larger fictional city.
-
D.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
E.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
- 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_69e245ba7ae48190be606dbc54120e39 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1867dfac48190bf300f85d2907854 |
completed | April 29, 2026, 4:18 a.m. |
| PD | Predicate disambiguation | batch_69ef89d5f71881908b9f9d0c8aab278c |
completed | April 27, 2026, 4:07 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:54 p.m.