Triple
T32877277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mockingbird Heights |
E840961
|
entity |
| Predicate | hasFictionalMunicipalType |
P192209
|
FINISHED |
| Object | suburban neighborhood |
—
|
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: suburban neighborhood | Statement: [Mockingbird Heights, hasFictionalMunicipalType, suburban neighborhood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalMunicipalType Context triple: [Mockingbird Heights, hasFictionalMunicipalType, suburban neighborhood]
-
A.
hasFictionalTownType
chosen
Indicates that a fictional town is classified as being of a particular type or category.
-
B.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
C.
hasMunicipalityType
Indicates that an administrative unit is classified as having a specific type or category of municipality (e.g., city, town, village).
-
D.
hasFictionalCountySeatRole
Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
-
E.
hasFictionalCityContext
Indicates that something is associated with, set in, or contextualized by a 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_69f349436ee88190b72ee12d0f3f508e |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fef5cf8da881908260ec633830375d |
completed | May 9, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_69fef455e40481909861c82007b79bc0 |
completed | May 9, 2026, 8:46 a.m. |
Created at: May 1, 2026, 1:18 a.m.