Triple
T28092096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Twin Pines Mall |
E709980
|
entity |
| Predicate | partOfFictionalTownInfrastructure |
P14483
|
FINISHED |
| Object | Hill Valley commercial district |
—
|
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: Hill Valley commercial district | Statement: [Twin Pines Mall, partOfFictionalTownInfrastructure, Hill Valley commercial district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfFictionalTownInfrastructure Context triple: [Twin Pines Mall, partOfFictionalTownInfrastructure, Hill Valley commercial district]
-
A.
partOfFictionalCityInfrastructure
Indicates that something is a component or subsystem within the constructed infrastructure of a fictional city.
-
B.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
C.
hasFictionalTownType
Indicates that a fictional town is classified as being of a particular type or category.
-
D.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
E.
partOfFictionalCity
chosen
Indicates that one entity is a component, area, or subdivision within a larger fictional city.
- 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_69ef9b70fd108190a875953b2e50ca91 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69fdbaa226708190b8ed96e93aad38de |
completed | May 8, 2026, 10:27 a.m. |
| PD | Predicate disambiguation | batch_69fdb58b07e48190837e00966de050d4 |
completed | May 8, 2026, 10:06 a.m. |
Created at: April 27, 2026, 8:59 p.m.