Triple
T9795622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Little People |
E237709
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Michael Ford
Michael Ford is an actor known for his role in the film "The Little People."
|
E823195
|
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: Michael Ford | Statement: [The Little People, castMember, Michael Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Ford Context triple: [The Little People, castMember, Michael Ford]
-
A.
Michael Gerald Ford
Michael Gerald Ford is the eldest son of former U.S. President Gerald Ford and First Lady Betty Ford, known for his low-profile public life and work in banking and nonprofit organizations.
-
B.
Daniel Ford
Daniel Ford was a 19th-century American editor and publisher best known for shaping the influential family magazine The Youth's Companion.
-
C.
Sam Ford
Sam Ford is the son of Nathan Ford, the central mastermind character from the television series "Leverage."
-
D.
Fred Ford
Fred Ford is a video game designer and programmer best known as the co-creator of the Star Control series and co-founder of the game development studio Toys for Bob.
-
E.
Jonathan Ford
Jonathan Ford is a senior naval officer and executive officer aboard the seaQuest DSV in the science fiction television series "seaQuest DSV."
- 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: Michael Ford Triple: [The Little People, castMember, Michael Ford]
Generated description
Michael Ford is an actor known for his role in the film "The Little People."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Ford Target entity description: Michael Ford is an actor known for his role in the film "The Little People."
-
A.
Michael Gerald Ford
Michael Gerald Ford is the eldest son of former U.S. President Gerald Ford and First Lady Betty Ford, known for his low-profile public life and work in banking and nonprofit organizations.
-
B.
Daniel Ford
Daniel Ford was a 19th-century American editor and publisher best known for shaping the influential family magazine The Youth's Companion.
-
C.
Sam Ford
Sam Ford is the son of Nathan Ford, the central mastermind character from the television series "Leverage."
-
D.
Fred Ford
Fred Ford is a video game designer and programmer best known as the co-creator of the Star Control series and co-founder of the game development studio Toys for Bob.
-
E.
Jonathan Ford
Jonathan Ford is a senior naval officer and executive officer aboard the seaQuest DSV in the science fiction television series "seaQuest DSV."
- 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda34916dc8190acef2ba003e56a33 |
completed | April 1, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc5118a481908a65d730f86c7723 |
completed | April 5, 2026, 2:43 a.m. |
| NEDg | Description generation | batch_69d1cce3d9d481909eaf7278dfe20955 |
completed | April 5, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1cd5d1670819085c58ff8889318af |
completed | April 5, 2026, 2:47 a.m. |
Created at: March 30, 2026, 8:28 p.m.