Triple
T12333450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The In Crowd |
E294019
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Mark Gibson
Mark Gibson is a screenwriter best known for co-writing the thriller film "The In Crowd."
|
E994016
|
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: Mark Gibson | Statement: [The In Crowd, screenwriter, Mark Gibson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Gibson Context triple: [The In Crowd, screenwriter, Mark Gibson]
-
A.
Graeme Gibson
Graeme Gibson was a Canadian novelist, environmentalist, and cultural advocate known for his contributions to Canadian literature and his long partnership with writer Margaret Atwood.
-
B.
Derek Gibson
Derek Gibson is a film producer best known for his work on the 1988 cult dark comedy thriller "Miracle Mile."
-
C.
John Gibson
John Gibson is an American professional ice hockey goaltender best known for his standout NHL career with the Anaheim Ducks and international play for Team USA.
-
D.
John Gibson
John Gibson was a 19th-century British architect known for designing prominent public buildings in a classical style.
-
E.
Michel Gibson
Michel Gibson is a local political figure who serves as the mayor of Kirkland, overseeing the city's municipal government and public affairs.
- 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: Mark Gibson Triple: [The In Crowd, screenwriter, Mark Gibson]
Generated description
Mark Gibson is a screenwriter best known for co-writing the thriller film "The In Crowd."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Gibson Target entity description: Mark Gibson is a screenwriter best known for co-writing the thriller film "The In Crowd."
-
A.
Graeme Gibson
Graeme Gibson was a Canadian novelist, environmentalist, and cultural advocate known for his contributions to Canadian literature and his long partnership with writer Margaret Atwood.
-
B.
Derek Gibson
Derek Gibson is a film producer best known for his work on the 1988 cult dark comedy thriller "Miracle Mile."
-
C.
John Gibson
John Gibson is an American professional ice hockey goaltender best known for his standout NHL career with the Anaheim Ducks and international play for Team USA.
-
D.
John Gibson
John Gibson was a 19th-century British architect known for designing prominent public buildings in a classical style.
-
E.
Michel Gibson
Michel Gibson is a local political figure who serves as the mayor of Kirkland, overseeing the city's municipal government and public affairs.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f64ad20819080d99e57833b4b51 |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65e9e909081909b341398e7aae954 |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f6637f6b188190b61c986aa37bcfed |
completed | May 2, 2026, 8:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f664db08e48190919ab5a175a23275 |
completed | May 2, 2026, 8:55 p.m. |
Created at: April 8, 2026, 9:53 p.m.