Triple
T26947788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langley Falls Police Department |
E678694
|
entity |
| Predicate | municipalityOfFictionalSetting |
P49286
|
FINISHED |
| Object | Langley Falls, Virginia |
—
|
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: Langley Falls, Virginia | Statement: [Langley Falls Police Department, municipalityOfFictionalSetting, Langley Falls, Virginia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: municipalityOfFictionalSetting Context triple: [Langley Falls Police Department, municipalityOfFictionalSetting, Langley Falls, Virginia]
-
A.
cityOfFictionalLocation
Indicates that a fictional location is situated within or associated with a particular city.
-
B.
cityOfFictionalResidence
Indicates that a fictional character or entity resides in, or is associated with living in, a particular city within a narrative or fictional context.
-
C.
cityOfFictionalActivity
Indicates that a fictional activity, event, or storyline takes place in the specified city.
-
D.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
E.
basedInFictionalLocation
chosen
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
- 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_69eeeb4d69588190a7c912164a1c37b3 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6383625cc8190aa223d8ef655743c |
completed | May 2, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69f63709e4848190b5cf322e06b23fb6 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 6:22 a.m.