Triple
T16178776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esperanto literature |
E392633
|
entity |
| Predicate | hasNotableWork |
P4
|
FINISHED |
| Object |
Metropoliteno
Metropoliteno is a notable Esperanto-language literary work, recognized as a significant contribution to modern Esperanto literature.
|
E1199889
|
NE FINISHED |
How this triple was built (4 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: Metropoliteno | Statement: [Esperanto literature, hasNotableWork, Metropoliteno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metropoliteno Context triple: [Esperanto literature, hasNotableWork, Metropoliteno]
-
A.
Metropolitana
Metropolitana is the principal coastal metropolitan region of the Brazilian state of Espírito Santo, encompassing its capital and surrounding urban areas.
-
B.
Metropolitano
Metropolitano is a former operator of the San Martín Line, a railway service in Argentina.
-
C.
Rio Metro
Rio Metro is the public transportation brand serving the Albuquerque metropolitan area and surrounding communities in central New Mexico, providing commuter rail and bus services.
-
D.
Metros
Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
-
E.
Metrorail
Metrorail is Miami-Dade County’s elevated rapid transit system that connects key neighborhoods, suburbs, and downtown Miami.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Metropoliteno Triple: [Esperanto literature, hasNotableWork, Metropoliteno]
Generated description
Metropoliteno is a notable Esperanto-language literary work, recognized as a significant contribution to modern Esperanto literature.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Metropoliteno Target entity description: Metropoliteno is a notable Esperanto-language literary work, recognized as a significant contribution to modern Esperanto literature.
-
A.
Metropolitana
Metropolitana is the principal coastal metropolitan region of the Brazilian state of Espírito Santo, encompassing its capital and surrounding urban areas.
-
B.
Metropolitano
Metropolitano is a former operator of the San Martín Line, a railway service in Argentina.
-
C.
Rio Metro
Rio Metro is the public transportation brand serving the Albuquerque metropolitan area and surrounding communities in central New Mexico, providing commuter rail and bus services.
-
D.
Metros
Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
-
E.
Metrorail
Metrorail is Miami-Dade County’s elevated rapid transit system that connects key neighborhoods, suburbs, and downtown Miami.
- F. None of above. chosen
Provenance (5 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2205ab3108190a84fc2dfe61d5044 |
completed | April 17, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fffefe4dc08190a6cc43a448ae6554 |
completed | May 10, 2026, 3:43 a.m. |
| NEDg | Description generation | batch_6a0000a8a74c8190925c4140cf4a8520 |
completed | May 10, 2026, 3:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0004ceda8c8190a358f58f76116a7f |
completed | May 10, 2026, 4:08 a.m. |
Created at: April 10, 2026, 5:02 a.m.