Triple
T21447555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frenchman’s Bend |
E529115
|
entity |
| Predicate | fictionalGeographicFeature |
P71480
|
FINISHED |
| Object | farms |
—
|
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: farms | Statement: [Frenchman’s Bend, fictionalGeographicFeature, farms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalGeographicFeature Context triple: [Frenchman’s Bend, fictionalGeographicFeature, farms]
-
A.
fictionalCountryLocation
Indicates that a fictional country is located within, or geographically associated with, a specified place or region.
-
B.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
-
C.
fictionalPlaceType
chosen
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
D.
refersToGeographicFeature
Indicates that one entity makes reference to, denotes, or is associated with a specific geographic feature such as a landform, body of water, or other physical location.
-
E.
depictsFictionalPlace
Indicates that one entity visually represents or portrays a place that exists only in fiction rather than in 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d04548819086594c20faa5217d |
completed | April 23, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:06 p.m.