Triple
T26947794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langley Falls Police Department |
E678694
|
entity |
| Predicate | partOfFictionalLocation |
P14483
|
FINISHED |
| Object | Langley Falls |
—
|
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 | Statement: [Langley Falls Police Department, partOfFictionalLocation, Langley Falls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfFictionalLocation Context triple: [Langley Falls Police Department, partOfFictionalLocation, Langley Falls]
-
A.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
B.
partOfFictionalCity
chosen
Indicates that one entity is a component, area, or subdivision within a larger fictional city.
-
C.
basedInFictionalWorkLocation
Indicates that an entity’s location or setting is situated within a fictional place as depicted in a specific creative work.
-
D.
fictionalUniverseLocation
Indicates that one entity is a location or setting within the fictional universe to which the other entity belongs or in which it takes place.
-
E.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
- 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_69fd02680d948190a3463fb119ba8556 |
completed | May 7, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69fcf89c69b4819082bbc564bd15137d |
completed | May 7, 2026, 8:39 p.m. |
Created at: April 27, 2026, 6:22 a.m.