Triple
T37708377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Roque, Cádiz |
E939254
|
entity |
| Predicate | hasBayShoreline |
P26435
|
FINISHED |
| Object | Bay of Algeciras (Bay of Gibraltar) |
—
|
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: Bay of Algeciras (Bay of Gibraltar) | Statement: [San Roque, Cádiz, hasBayShoreline, Bay of Algeciras (Bay of Gibraltar)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBayShoreline Context triple: [San Roque, Cádiz, hasBayShoreline, Bay of Algeciras (Bay of Gibraltar)]
-
A.
hasLongShoreline
Indicates that an entity possesses an extensive or unusually long shoreline relative to typical cases.
-
B.
hasScenicShoreline
Indicates that something possesses a shoreline characterized by notable natural beauty or visually appealing scenery.
-
C.
hasShoreNear
Indicates that one entity is located close enough to another entity’s shore or coastline to be considered nearby.
-
D.
hasShoreOn
chosen
Indicates that one geographic entity borders or is directly adjacent to the shore of another body of water.
-
E.
hasShorelineMunicipality
Indicates that a municipality is located along and directly borders the shoreline of a body of water.
- 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_69f76edb49dc8190b951dce9ce6ef789 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: May 3, 2026, 4:18 p.m.