Triple
T30817330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Germain-de-Varreville |
E784816
|
entity |
| Predicate | nearbyLandingZone |
P61270
|
FINISHED |
| Object | Utah Beach landing sector |
—
|
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: Utah Beach landing sector | Statement: [Saint-Germain-de-Varreville, nearbyLandingZone, Utah Beach landing sector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyLandingZone Context triple: [Saint-Germain-de-Varreville, nearbyLandingZone, Utah Beach landing sector]
-
A.
nearbyLocation
chosen
Indicates that one location is situated close to another location in physical space.
-
B.
nearestInhabitedTerritory
Indicates that one territory is the closest inhabited territory to another specified location or territory.
-
C.
nearbyAirportAccess
Indicates that an entity has convenient access to an airport located within a short distance or travel time.
-
D.
nearbyAirportRelationship
Indicates that one location has an airport situated close enough to serve it conveniently, establishing a nearby-airport relationship between the two.
-
E.
hasNearbyGeneralAviationAirport
Indicates that an entity is located close to a general aviation airport, such that the airport can reasonably serve it for non-commercial or private air traffic.
- 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_69f224b4eda48190bd212ce4f3901e56 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
Created at: April 29, 2026, 8:44 p.m.