Triple
T13726020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Feel Good |
E329654
|
entity |
| Predicate | performer |
P1363
|
FINISHED |
| Object |
Christopher Smith
Christopher Smith is an actor known for his performance in the film "Feel Good."
|
E1059499
|
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: Christopher Smith | Statement: [Feel Good, performer, Christopher Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christopher Smith Context triple: [Feel Good, performer, Christopher Smith]
-
A.
Christopher Smith
Christopher Smith is a relative of English actor Jeremy Irvine, known for his role in the film "War Horse."
-
B.
Christopher Smith
Christopher Smith is an individual associated with online communities or organizations connected to the Internet.
-
C.
Leo Smith
Leo Smith is an American avant-garde jazz trumpeter and composer known for his innovative contributions to creative and experimental music.
-
D.
Leo Smith
Leo Smith is an education administrator who serves as the business administrator for the Bayonne School District, overseeing its financial and operational affairs.
-
E.
Mark Smith
Mark Smith is a renowned designer known for his influential work with Nike, including creating iconic basketball-related trophies and products.
- 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: Christopher Smith Triple: [Feel Good, performer, Christopher Smith]
Generated description
Christopher Smith is an actor known for his performance in the film "Feel Good."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Christopher Smith Target entity description: Christopher Smith is an actor known for his performance in the film "Feel Good."
-
A.
Christopher Smith
Christopher Smith is an individual associated with online communities or organizations connected to the Internet.
-
B.
Christopher Smith
Christopher Smith is a relative of English actor Jeremy Irvine, known for his role in the film "War Horse."
-
C.
Leo Smith
Leo Smith is an American avant-garde jazz trumpeter and composer known for his innovative contributions to creative and experimental music.
-
D.
Leo Smith
Leo Smith is an education administrator who serves as the business administrator for the Bayonne School District, overseeing its financial and operational affairs.
-
E.
Mark Smith
Mark Smith is a renowned designer known for his influential work with Nike, including creating iconic basketball-related trophies and products.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f63c1c8190a7d0b84f319aa99b |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a84c02e08190b8ef620575157c14 |
completed | May 3, 2026, 7:55 p.m. |
| NEDg | Description generation | batch_69f7a994cd688190a077a4854c5c71c9 |
completed | May 3, 2026, 8:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7aa2f696081908f48d44bf7271abc |
completed | May 3, 2026, 8:03 p.m. |
Created at: April 9, 2026, 9:55 p.m.