Triple
T14878807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Data |
E349938
|
entity |
| Predicate | friendOf |
P8712
|
FINISHED |
| Object |
Andy Carmichael
Andy Carmichael is a person associated with the character Data, likely within the context of the film "The Goonies."
|
E1125310
|
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: Andy Carmichael | Statement: [Data, friendOf, Andy Carmichael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andy Carmichael Context triple: [Data, friendOf, Andy Carmichael]
-
A.
Michael Durkan
Michael Durkan is a notable individual who shares the Durkan surname, recognized as a distinguished bearer of that family name.
-
B.
Sam Barrington
Sam Barrington is an American former NFL linebacker who played primarily for the Green Bay Packers after a standout college career at the University of South Florida.
-
C.
Andrew Brice
Andrew Brice is an Australian entrepreneur best known as the co-founder of the online travel company Wotif Group.
-
D.
Ian Crafford
Ian Crafford is a film editor best known for his work on the James Bond movie "Never Say Never Again."
-
E.
Andy Knightley
Andy Knightley is a straight-laced, teetotal lawyer and former friend of the protagonist who is reluctantly drawn into a disastrous pub crawl in the sci-fi comedy film "The World's End."
- 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: Andy Carmichael Triple: [Data, friendOf, Andy Carmichael]
Generated description
Andy Carmichael is a person associated with the character Data, likely within the context of the film "The Goonies."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Andy Carmichael Target entity description: Andy Carmichael is a person associated with the character Data, likely within the context of the film "The Goonies."
-
A.
Michael Durkan
Michael Durkan is a notable individual who shares the Durkan surname, recognized as a distinguished bearer of that family name.
-
B.
Sam Barrington
Sam Barrington is an American former NFL linebacker who played primarily for the Green Bay Packers after a standout college career at the University of South Florida.
-
C.
Andrew Brice
Andrew Brice is an Australian entrepreneur best known as the co-founder of the online travel company Wotif Group.
-
D.
Ian Crafford
Ian Crafford is a film editor best known for his work on the James Bond movie "Never Say Never Again."
-
E.
Andy Knightley
Andy Knightley is a straight-laced, teetotal lawyer and former friend of the protagonist who is reluctantly drawn into a disastrous pub crawl in the sci-fi comedy film "The World's End."
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e622388190b2bf91cd10b9821d |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b5670108190b41ef95dc318be60 |
completed | May 8, 2026, 11:01 p.m. |
| NEDg | Description generation | batch_69fe6d3aa2f08190b02c6157c03a2ba5 |
completed | May 8, 2026, 11:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6db30e9c81908fbad7b932799a7a |
completed | May 8, 2026, 11:11 p.m. |
Created at: April 10, 2026, 1:55 a.m.