Triple
T10900604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Bordeaux |
E257428
|
entity |
| Predicate | hasPortArea |
P64085
|
FINISHED |
| Object | Bassens |
E874614
|
NE 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: Bassens | Statement: [Port of Bordeaux, hasPortArea, Bassens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bassens Context triple: [Port of Bordeaux, hasPortArea, Bassens]
-
A.
Bassens
chosen
Bassens is a commune in southwestern France, located near the city of Bordeaux in the Gironde department.
-
B.
Berbeka
Berbeka is a Polish surname most notably associated with high-altitude mountaineer Maciej Berbeka.
-
C.
Baré
Baré is a Brazilian professional footballer known as a prolific striker who has played for several clubs in Asia, including a notable spell in the Chinese Super League.
-
D.
Kaloum
Kaloum is the central urban commune of Conakry, Guinea, encompassing the city’s historic core, main government institutions, and port area.
-
E.
Bensafrim
Bensafrim is a village and former civil parish in the municipality of Lagos, in Portugal’s Algarve region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d761a2f02881908b70be6499dd8d98 |
completed | April 9, 2026, 8:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e155306e9081909433522eeecf2b7d |
completed | April 16, 2026, 9:31 p.m. |
Created at: April 8, 2026, 9:22 p.m.