Triple
T12794297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adrian Grunberg |
E305849
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Get the Gringo
Get the Gringo is a 2012 action crime film starring Mel Gibson as a career criminal navigating the dangers of a corrupt Mexican prison.
|
E1003174
|
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: Get the Gringo | Statement: [Adrian Grunberg, knownFor, Get the Gringo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Get the Gringo Context triple: [Adrian Grunberg, knownFor, Get the Gringo]
-
A.
Gringo
"Gringo" is a popular dancehall song by Ghanaian artist Shatta Wale, known for its catchy hook and cinematic Western-themed music video.
-
B.
Gringo
Gringo is a dark comedy–crime film in which Paris Jackson appears alongside an ensemble cast in a story about a businessman caught up in a dangerous scheme in Mexico.
-
C.
GRO
GRO is the FAA airport code assigned to Rota International Airport, a public airport serving the island of Rota in the Northern Mariana Islands.
-
D.
GRO
GRO is the station code used to identify Grove Street station on the Newark Light Rail system in New Jersey.
-
E.
GRO
GRO is the commonly used acronym for the Compton Gamma Ray Observatory, a NASA space telescope that studied high-energy gamma-ray sources in the universe.
- 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: Get the Gringo Triple: [Adrian Grunberg, knownFor, Get the Gringo]
Generated description
Get the Gringo is a 2012 action crime film starring Mel Gibson as a career criminal navigating the dangers of a corrupt Mexican prison.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Get the Gringo Target entity description: Get the Gringo is a 2012 action crime film starring Mel Gibson as a career criminal navigating the dangers of a corrupt Mexican prison.
-
A.
Gringo
"Gringo" is a popular dancehall song by Ghanaian artist Shatta Wale, known for its catchy hook and cinematic Western-themed music video.
-
B.
Gringo
Gringo is a dark comedy–crime film in which Paris Jackson appears alongside an ensemble cast in a story about a businessman caught up in a dangerous scheme in Mexico.
-
C.
GRO
GRO is the FAA airport code assigned to Rota International Airport, a public airport serving the island of Rota in the Northern Mariana Islands.
-
D.
GRO
GRO is the station code used to identify Grove Street station on the Newark Light Rail system in New Jersey.
-
E.
GRO
GRO is the commonly used acronym for the Compton Gamma Ray Observatory, a NASA space telescope that studied high-energy gamma-ray sources in the universe.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6ca0288190aba01735b71a01da |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6850ac1808190a9b547d934252d10 |
completed | May 2, 2026, 11:13 p.m. |
| NEDg | Description generation | batch_69f689733f748190bca592ab30b4437c |
completed | May 2, 2026, 11:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f68a4cb2c4819083def0a43452470f |
completed | May 2, 2026, 11:35 p.m. |
Created at: April 9, 2026, 5:30 p.m.