Triple
T2964172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amorgos |
E80120
|
entity |
| Predicate | usedAsFilmLocationFor |
P795
|
FINISHED |
| Object |
The Big Blue
The Big Blue is a 1988 French film by Luc Besson that follows the intense rivalry and friendship between two champion free divers against the backdrop of the Mediterranean Sea.
|
E314640
|
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: The Big Blue | Statement: [Amorgos, usedAsFilmLocationFor, The Big Blue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Big Blue Context triple: [Amorgos, usedAsFilmLocationFor, The Big Blue]
-
A.
The Big Tuna
The Big Tuna is the famous nickname of Bill Parcells, a Hall of Fame NFL head coach known for turning struggling teams into contenders.
-
B.
Big Blue
Big Blue is the costumed mascot character representing Bluefield State University at its athletic events and campus activities.
-
C.
Big Blue
Big Blue is the widely used nickname for the New York Giants, a professional American football team in the NFL.
-
D.
Bluewater
Bluewater is a large out-of-town shopping and leisure centre located in Kent, England.
-
E.
El Gran Pez
El Gran Pez is the popular nickname of the Mexican football club Dorados de Sinaloa, reflecting its identity and local cultural ties.
- 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: The Big Blue Triple: [Amorgos, usedAsFilmLocationFor, The Big Blue]
Generated description
The Big Blue is a 1988 French film by Luc Besson that follows the intense rivalry and friendship between two champion free divers against the backdrop of the Mediterranean Sea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: The Big Blue Target entity description: The Big Blue is a 1988 French film by Luc Besson that follows the intense rivalry and friendship between two champion free divers against the backdrop of the Mediterranean Sea.
-
A.
The Big Tuna
The Big Tuna is the famous nickname of Bill Parcells, a Hall of Fame NFL head coach known for turning struggling teams into contenders.
-
B.
Big Blue
Big Blue is the costumed mascot character representing Bluefield State University at its athletic events and campus activities.
-
C.
Big Blue
Big Blue is the widely used nickname for the New York Giants, a professional American football team in the NFL.
-
D.
Bluewater
Bluewater is a large out-of-town shopping and leisure centre located in Kent, England.
-
E.
El Gran Pez
El Gran Pez is the popular nickname of the Mexican football club Dorados de Sinaloa, reflecting its identity and local cultural ties.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9958b1e48190a77f37bf63333c5b |
completed | March 8, 2026, 3:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc98d94481908282d21394dc24a7 |
completed | March 11, 2026, 5:24 a.m. |
| NEDg | Description generation | batch_69b0fd07b82881908d52ab2db2f2e54c |
completed | March 11, 2026, 5:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0fdba0fd88190a2e760f1770e846c |
completed | March 11, 2026, 5:29 a.m. |
Created at: March 8, 2026, 2:58 p.m.