Triple
T26959119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy Biggins |
E678986
|
entity |
| Predicate | worksAtFictionalLocation |
P125184
|
FINISHED |
| Object | Nantucket Memorial Airport |
—
|
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: Nantucket Memorial Airport | Statement: [Roy Biggins, worksAtFictionalLocation, Nantucket Memorial Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worksAtFictionalLocation Context triple: [Roy Biggins, worksAtFictionalLocation, Nantucket Memorial Airport]
-
A.
worksAtFictionalPlace
chosen
Indicates that an entity is employed at or associated with performing work in a fictional or imaginary location.
-
B.
basedInFictionalWorkLocation
Indicates that an entity’s location or setting is situated within a fictional place as depicted in a specific creative work.
-
C.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
D.
hasBranchInFictionalLocation
Indicates that an organization maintains a branch, office, or presence within a fictional or imaginary location.
-
E.
residesInFictionalLocation
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
- 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_69eeeb4e75f08190b14fc91ca4a91488 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f674e06c9481909ed0ea736408f0d7 |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f673c2f81c8190bf369226306eef09 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 27, 2026, 6:29 a.m.