Triple
T13827427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clan Colquhoun |
E332287
|
entity |
| Predicate | hasSurnameVariant |
P457
|
FINISHED |
| Object |
Cahoon
Cahoon is a surname that originated as a variant of the Scottish Clan Colquhoun name.
|
E1064494
|
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: Cahoon | Statement: [Clan Colquhoun, hasSurnameVariant, Cahoon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cahoon Context triple: [Clan Colquhoun, hasSurnameVariant, Cahoon]
-
A.
Kinnear
Kinnear is an English surname most notably associated with the British actor Rory Kinnear and his theatrical family.
-
B.
Reeser
Reeser is a surname most notably associated with American actress Autumn Reeser, known for her roles in television and film.
-
C.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
-
D.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
E.
Mitch McDeere
Mitch McDeere is an ambitious young Harvard-educated lawyer who becomes entangled in a corrupt law firm’s criminal activities in John Grisham’s legal thriller "The Firm."
- 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: Cahoon Triple: [Clan Colquhoun, hasSurnameVariant, Cahoon]
Generated description
Cahoon is a surname that originated as a variant of the Scottish Clan Colquhoun name.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cahoon Target entity description: Cahoon is a surname that originated as a variant of the Scottish Clan Colquhoun name.
-
A.
Kinnear
Kinnear is an English surname most notably associated with the British actor Rory Kinnear and his theatrical family.
-
B.
Reeser
Reeser is a surname most notably associated with American actress Autumn Reeser, known for her roles in television and film.
-
C.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
-
D.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
E.
Mitch McDeere
Mitch McDeere is an ambitious young Harvard-educated lawyer who becomes entangled in a corrupt law firm’s criminal activities in John Grisham’s legal thriller "The Firm."
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0295d2d48190b08eba0d805bd72d |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8ea22c081909cc34f1030a8589b |
completed | May 3, 2026, 9:06 p.m. |
| NEDg | Description generation | batch_69f7ba6558fc819082dae55863a9a3b1 |
completed | May 3, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7bb15d31c81909959e50219c4d905 |
completed | May 3, 2026, 9:16 p.m. |
Created at: April 9, 2026, 10:13 p.m.