Triple
T23794123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Buenos Aires |
E588487
|
entity |
| Predicate | hasApproximateUNLocode |
P1492
|
FINISHED |
| Object |
ARBUE
ARBUE is the UN/LOCODE identifier for the Port of Buenos Aires in Argentina, used in international trade and transport logistics.
|
E1600839
|
NE FINISHED |
How this triple was built (3 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: ARBUE | Statement: [Port of Buenos Aires, hasApproximateUNLocode, ARBUE]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ARBUE Triple: [Port of Buenos Aires, hasApproximateUNLocode, ARBUE]
Generated description
ARBUE is the UN/LOCODE identifier for the Port of Buenos Aires in Argentina, used in international trade and transport logistics.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateUNLocode Context triple: [Port of Buenos Aires, hasApproximateUNLocode, ARBUE]
-
A.
hasUNLocode
chosen
Indicates that an entity is associated with a specific UN/LOCODE, identifying its location in the United Nations location code system.
-
B.
hasICAOAirportCodeNearby
Indicates that an entity is located near, or is associated with, an airport identified by a specific ICAO airport code.
-
C.
hasRelativePositionAtAirport
Indicates that one entity has a specific spatial or positional relationship to another entity within the context or layout of an airport.
-
D.
hasAirportAccessTo
Indicates that one location or entity has direct access to another via an airport connection or service.
-
E.
isCargoAirportCode
Indicates that an airport code specifically designates an airport primarily used for cargo operations.
- F. None of above.
Provenance (6 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_69e25d15db58819092ac1e6791696fd9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c6da693481908194cbc9d6a0bfef |
completed | April 29, 2026, 8:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f53f346e08190adb7e1af0f2ca21e |
completed | May 21, 2026, 6:50 p.m. |
| NEDg | Description generation | batch_6a0f5634499c8190ac46621942ed2fd3 |
completed | May 21, 2026, 7 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f56b2407c8190aa957702cf33267c |
completed | May 21, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:42 p.m.