Triple
T38093489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | village of Sounio |
E951174
|
entity |
| Predicate | hasNearbyMaritimeFeature |
P117087
|
FINISHED |
| Object | Cape Sounion headland |
—
|
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: Cape Sounion headland | Statement: [village of Sounio, hasNearbyMaritimeFeature, Cape Sounion headland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyMaritimeFeature Context triple: [village of Sounio, hasNearbyMaritimeFeature, Cape Sounion headland]
-
A.
hasNearbyMarineFeature
chosen
Indicates that one entity is located close to a marine geographic feature associated with the other entity.
-
B.
hasMaritimeFeature
Indicates that one entity possesses, contains, or is characterized by a maritime-related feature such as a sea, coast, harbor, or other oceanic element.
-
C.
hasNearbyCoast
Indicates that one location is situated close to a coastline or seashore.
-
D.
hasNearbyWater
Indicates that one entity is located close to a body of water associated with or relevant to another entity.
-
E.
hasShoreNear
Indicates that one entity is located close enough to another entity’s shore or coastline to be considered nearby.
- 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_69f76f04960c8190a83f14ae4c67f5bc |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a00bcfed6448190b2e816bbe7c61c55 |
completed | May 10, 2026, 5:14 p.m. |
| PD | Predicate disambiguation | batch_6a00bc7be24c81908ba5c1957edd2c10 |
completed | May 10, 2026, 5:12 p.m. |
Created at: May 3, 2026, 4:21 p.m.