Triple
T301716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benghazi |
E6209
|
entity |
| Predicate | hasPortFunction |
P2745
|
FINISHED |
| Object | commercial shipping |
—
|
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: commercial shipping | Statement: [Benghazi, hasPortFunction, commercial shipping]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPortFunction Context triple: [Benghazi, hasPortFunction, commercial shipping]
-
A.
hasMajorPort
Indicates that a location possesses a primary, significant seaport used for major commercial or transportation activities.
-
B.
hasPortico
Indicates that one entity (typically a building or structure) features a portico as part of its architectural design.
-
C.
hasPortCity
chosen
Indicates that a place or region possesses or is associated with a city that functions as its port.
-
D.
hasPrimaryFunction
Indicates that one entity serves as the main or principal function or role of another entity.
-
E.
hasDistributionFunction
Indicates that an entity is associated with a specific distribution function that characterizes how its values or occurrences are probabilistically or statistically distributed.
- 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_69a2e79230508190b912ecb555aae17e |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea2fba548190a5aeb1597dca96bd |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e93c367881908d3f6e2b81d44d7f |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.