Triple
T11983173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belle |
E285208
|
entity |
| Predicate | romanticPartner |
P9994
|
FINISHED |
| Object |
Prince Adam
Prince Adam is the human identity of the Beast, the cursed prince who falls in love with Belle in Disney's "Beauty and the Beast."
|
E958207
|
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: Prince Adam | Statement: [Belle, romanticPartner, Prince Adam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prince Adam Context triple: [Belle, romanticPartner, Prince Adam]
-
A.
King Ralph
King Ralph is a 1991 comedy film in which John Goodman plays an uncouth American who unexpectedly becomes the king of the United Kingdom after the entire royal family is killed in a freak accident.
-
B.
Peter Kingdom
Peter Kingdom is the mild-mannered, compassionate solicitor protagonist of the British television drama series "Kingdom," set in a small Norfolk town.
-
C.
Geoffrey Keen
Geoffrey Keen was a British character actor best known for his recurring role as the Minister of Defence in the James Bond film series.
-
D.
Rupert
Rupert is a small town located in Greenbrier County in the state of West Virginia, United States.
-
E.
Rupert
Rupert is a small agricultural city in south-central Idaho known for its historic town square and role as a local farming hub.
- 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: Prince Adam Triple: [Belle, romanticPartner, Prince Adam]
Generated description
Prince Adam is the human identity of the Beast, the cursed prince who falls in love with Belle in Disney's "Beauty and the Beast."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prince Adam Target entity description: Prince Adam is the human identity of the Beast, the cursed prince who falls in love with Belle in Disney's "Beauty and the Beast."
-
A.
King Ralph
King Ralph is a 1991 comedy film in which John Goodman plays an uncouth American who unexpectedly becomes the king of the United Kingdom after the entire royal family is killed in a freak accident.
-
B.
Peter Kingdom
Peter Kingdom is the mild-mannered, compassionate solicitor protagonist of the British television drama series "Kingdom," set in a small Norfolk town.
-
C.
Geoffrey Keen
Geoffrey Keen was a British character actor best known for his recurring role as the Minister of Defence in the James Bond film series.
-
D.
Rupert
Rupert is a small town located in Greenbrier County in the state of West Virginia, United States.
-
E.
Rupert
Rupert is a small agricultural city in south-central Idaho known for its historic town square and role as a local farming hub.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903973c848190aac871d6dfecc74b |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f472286edc8190ac72d7dd2b646c91 |
completed | May 1, 2026, 9:28 a.m. |
| NEDg | Description generation | batch_69f47b7c5af08190ab0bff1232530a0c |
completed | May 1, 2026, 10:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f47dd51e648190bddd41766221e22d |
completed | May 1, 2026, 10:17 a.m. |
Created at: April 8, 2026, 9:46 p.m.