Triple
T12917005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viktor Navorski |
E309010
|
entity |
| Predicate | formsFriendshipWith |
P39937
|
FINISHED |
| Object |
Enrique Cruz
Enrique Cruz is a kindhearted airport food-service worker in the film "The Terminal" who befriends stranded traveler Viktor Navorski.
|
E1094911
|
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: Enrique Cruz | Statement: [Viktor Navorski, formsFriendshipWith, Enrique Cruz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Enrique Cruz Context triple: [Viktor Navorski, formsFriendshipWith, Enrique Cruz]
-
A.
Armando Bermúdez
Armando Bermúdez was a prominent Dominican figure after whom one of the country’s major national parks, located in the Cordillera Central, is named.
-
B.
Miguel Briseño
Miguel Briseño is a musician best known as a member of the American indie folk band Lord Huron.
-
C.
Victor Hernández Cruz
Victor Hernández Cruz is a Puerto Rican poet known for his innovative, jazz-influenced verse and his role as a prominent voice in contemporary Latino literature.
-
D.
Guillermo Magaña
Guillermo Magaña is a person notable enough to be recognized as a bearer of the surname Magaña, though specific widely known public details about him are not clearly established.
-
E.
Raúl Dávalos
Raúl Dávalos is an editor known for his work on the film "Cronos."
- 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: Enrique Cruz Triple: [Viktor Navorski, formsFriendshipWith, Enrique Cruz]
Generated description
Enrique Cruz is a kindhearted airport food-service worker in the film "The Terminal" who befriends stranded traveler Viktor Navorski.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Enrique Cruz Target entity description: Enrique Cruz is a kindhearted airport food-service worker in the film "The Terminal" who befriends stranded traveler Viktor Navorski.
-
A.
Armando Bermúdez
Armando Bermúdez was a prominent Dominican figure after whom one of the country’s major national parks, located in the Cordillera Central, is named.
-
B.
Miguel Briseño
Miguel Briseño is a musician best known as a member of the American indie folk band Lord Huron.
-
C.
Victor Hernández Cruz
Victor Hernández Cruz is a Puerto Rican poet known for his innovative, jazz-influenced verse and his role as a prominent voice in contemporary Latino literature.
-
D.
Guillermo Magaña
Guillermo Magaña is a person notable enough to be recognized as a bearer of the surname Magaña, though specific widely known public details about him are not clearly established.
-
E.
Raúl Dávalos
Raúl Dávalos is an editor known for his work on the film "Cronos."
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a1e8088190af697629baecf59f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c1ee7048190b2571364b25bd49d |
completed | May 8, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fd4cc76178819086fb9a9b6b5cfd05 |
completed | May 8, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd4dcc41c481908f0d7e05c4c176ee |
completed | May 8, 2026, 2:43 a.m. |
Created at: April 9, 2026, 5:41 p.m.