Triple
T13076275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Eszterhas |
E329583
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Hollywood Animal
Hollywood Animal is a memoir by screenwriter Joe Eszterhas recounting his tumultuous life and career in Hollywood.
|
E1019395
|
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: Hollywood Animal | Statement: [Joe Eszterhas, notableWork, Hollywood Animal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hollywood Animal Context triple: [Joe Eszterhas, notableWork, Hollywood Animal]
-
A.
Buggsy
Buggsy is the nickname of Martin "Buggsy" Goldstein, an American mobster associated with organized crime in the early 20th century.
-
B.
Roscoe the Lion
Roscoe the Lion is the official lion mascot representing The College of New Jersey at its athletic events and campus activities.
-
C.
Flicka
Flicka is a 2006 family drama film about a teenage girl and her bond with a wild mustang, adapted from the classic novel "My Friend Flicka."
-
D.
Fido
Fido is a 2006 Canadian zombie comedy film in which Carrie-Anne Moss plays a lead role in a 1950s-style world where domesticated zombies serve humans.
-
E.
Fido
Fido is a Canadian mobile phone service provider known for offering wireless plans and devices, primarily targeting value-conscious consumers.
- 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: Hollywood Animal Triple: [Joe Eszterhas, notableWork, Hollywood Animal]
Generated description
Hollywood Animal is a memoir by screenwriter Joe Eszterhas recounting his tumultuous life and career in Hollywood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hollywood Animal Target entity description: Hollywood Animal is a memoir by screenwriter Joe Eszterhas recounting his tumultuous life and career in Hollywood.
-
A.
Buggsy
Buggsy is the nickname of Martin "Buggsy" Goldstein, an American mobster associated with organized crime in the early 20th century.
-
B.
Roscoe the Lion
Roscoe the Lion is the official lion mascot representing The College of New Jersey at its athletic events and campus activities.
-
C.
Flicka
Flicka is a 2006 family drama film about a teenage girl and her bond with a wild mustang, adapted from the classic novel "My Friend Flicka."
-
D.
Fido
Fido is a 2006 Canadian zombie comedy film in which Carrie-Anne Moss plays a lead role in a 1950s-style world where domesticated zombies serve humans.
-
E.
Fido
Fido is a Canadian mobile phone service provider known for offering wireless plans and devices, primarily targeting value-conscious consumers.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98117209081908272021013df2222 |
completed | April 10, 2026, 11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d608a2288190bf07023a5303f887 |
completed | May 3, 2026, 4:58 a.m. |
| NEDg | Description generation | batch_69f6d6e326408190b7906c7ea8e3ef85 |
completed | May 3, 2026, 5:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6d873b978819097962c82e8ffdac8 |
completed | May 3, 2026, 5:09 a.m. |
Created at: April 9, 2026, 9:01 p.m.