Triple
T11234923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Caesar |
E265918
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Stanley Fields
Stanley Fields was an American character actor best known for his tough-guy roles in early Hollywood gangster films of the 1930s.
|
E913085
|
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: Stanley Fields | Statement: [Little Caesar, stars, Stanley Fields]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stanley Fields Context triple: [Little Caesar, stars, Stanley Fields]
-
A.
Stanley Ralph
Stanley Ralph is the son of American actress and singer Sheryl Lee Ralph.
-
B.
Stanley Crawford
Stanley Crawford is the skeptical English illusionist and main character in Woody Allen's romantic comedy film "Magic in the Moonlight."
-
C.
Stanley Brook
Stanley Brook is a small watercourse in Greater Manchester, England, that serves as one of the tributary streams feeding the River Roch.
-
D.
Stanley Ridges
Stanley Ridges was a British-born character actor known for his versatile supporting roles in classic Hollywood films of the 1930s and 1940s.
-
E.
Stanley Townsend
Stanley Townsend is an Irish character actor known for his extensive work in film, television, and theatre, often portraying complex supporting roles.
- 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: Stanley Fields Triple: [Little Caesar, stars, Stanley Fields]
Generated description
Stanley Fields was an American character actor best known for his tough-guy roles in early Hollywood gangster films of the 1930s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stanley Fields Target entity description: Stanley Fields was an American character actor best known for his tough-guy roles in early Hollywood gangster films of the 1930s.
-
A.
Stanley Ralph
Stanley Ralph is the son of American actress and singer Sheryl Lee Ralph.
-
B.
Stanley Crawford
Stanley Crawford is the skeptical English illusionist and main character in Woody Allen's romantic comedy film "Magic in the Moonlight."
-
C.
Stanley Brook
Stanley Brook is a small watercourse in Greater Manchester, England, that serves as one of the tributary streams feeding the River Roch.
-
D.
Stanley Ridges
Stanley Ridges was a British-born character actor known for his versatile supporting roles in classic Hollywood films of the 1930s and 1940s.
-
E.
Stanley Townsend
Stanley Townsend is an Irish character actor known for his extensive work in film, television, and theatre, often portraying complex supporting roles.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e903b8ec81909f9c89776d35c650 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad56013481909f931505824e3b42 |
completed | April 19, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69e4b12dd658819085c25d3edac2d66c |
completed | April 19, 2026, 10:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4b3e05b488190bf2e3810ba2f250e |
completed | April 19, 2026, 10:52 a.m. |
Created at: April 8, 2026, 9:30 p.m.