Triple
T16842021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Punt |
E409435
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Punt PI
Punt PI is a British radio comedy series in which comedian Steve Punt humorously investigates and discusses current events and topical issues in a mock-detective format.
|
E1237044
|
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: Punt PI | Statement: [Steve Punt, notableWork, Punt PI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Punt PI Context triple: [Steve Punt, notableWork, Punt PI]
-
A.
Punt
Punt was an ancient trading region, likely located along the Red Sea or Horn of Africa, famed in Egyptian records for its exports of incense, ebony, gold, and exotic animals.
-
B.
Punt
Punt is a surname of English origin most notably associated with British comedian and writer Steve Punt.
-
C.
E Pier
E Pier is one of the main passenger boarding concourses at Amsterdam Airport Schiphol, serving multiple gates for international flights.
-
D.
Puán
Puán is a station on Buenos Aires’ historic Line A subway, serving the Caballito neighborhood near the University of Buenos Aires’ Philosophy and Letters faculty.
-
E.
Pio
Pio is the costumed mascot representing the athletic teams and school spirit of Lewis & Clark College.
- 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: Punt PI Triple: [Steve Punt, notableWork, Punt PI]
Generated description
Punt PI is a British radio comedy series in which comedian Steve Punt humorously investigates and discusses current events and topical issues in a mock-detective format.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Punt PI Target entity description: Punt PI is a British radio comedy series in which comedian Steve Punt humorously investigates and discusses current events and topical issues in a mock-detective format.
-
A.
Punt
Punt was an ancient trading region, likely located along the Red Sea or Horn of Africa, famed in Egyptian records for its exports of incense, ebony, gold, and exotic animals.
-
B.
Punt
chosen
Punt is a surname of English origin most notably associated with British comedian and writer Steve Punt.
-
C.
E Pier
E Pier is one of the main passenger boarding concourses at Amsterdam Airport Schiphol, serving multiple gates for international flights.
-
D.
Puán
Puán is a station on Buenos Aires’ historic Line A subway, serving the Caballito neighborhood near the University of Buenos Aires’ Philosophy and Letters faculty.
-
E.
Pio
Pio is the costumed mascot representing the athletic teams and school spirit of Lewis & Clark College.
- F. None of above.
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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b35167a48190b45a459023e3ab1b |
completed | April 18, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c2a3296c8190978c1809264f66e1 |
completed | May 10, 2026, 5:38 p.m. |
| NEDg | Description generation | batch_6a00c332051c8190b086a6e29b8d9c61 |
completed | May 10, 2026, 5:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00c434d8f88190a71c1c4c8e475e33 |
completed | May 10, 2026, 5:45 p.m. |
Created at: April 10, 2026, 5:24 a.m.