Triple
T13950697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warren Skaaren |
E335514
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Skaaren
Skaaren is a surname most notably associated with Warren Skaaren, an American screenwriter and film producer.
|
E1071500
|
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: Skaaren | Statement: [Warren Skaaren, familyName, Skaaren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skaaren Context triple: [Warren Skaaren, familyName, Skaaren]
-
A.
Stetind
Stetind is a distinctive, obelisk-shaped granite mountain in Nordland, Norway, often called Norway’s national mountain and renowned among climbers and photographers.
-
B.
Skinnskatteberg
Skinnskatteberg is a small Swedish locality and municipal seat in central Sweden, known for its forested landscape and historical mining industry.
-
C.
Namsskogan
Namsskogan is a sparsely populated inland municipality in Trøndelag county, Norway, known for its vast forests, wildlife, and outdoor recreation opportunities.
-
D.
Thamerdal
Thamerdal is a residential neighborhood within the Dutch town of Uithoorn in the province of North Holland.
-
E.
Namdalseid
Namdalseid is a former rural municipality in Trøndelag county, Norway, known for its forests, agriculture, and coastal landscape along the Namsenfjorden.
- 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: Skaaren Triple: [Warren Skaaren, familyName, Skaaren]
Generated description
Skaaren is a surname most notably associated with Warren Skaaren, an American screenwriter and film producer.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Skaaren Target entity description: Skaaren is a surname most notably associated with Warren Skaaren, an American screenwriter and film producer.
-
A.
Stetind
Stetind is a distinctive, obelisk-shaped granite mountain in Nordland, Norway, often called Norway’s national mountain and renowned among climbers and photographers.
-
B.
Skinnskatteberg
Skinnskatteberg is a small Swedish locality and municipal seat in central Sweden, known for its forested landscape and historical mining industry.
-
C.
Namsskogan
Namsskogan is a sparsely populated inland municipality in Trøndelag county, Norway, known for its vast forests, wildlife, and outdoor recreation opportunities.
-
D.
Thamerdal
Thamerdal is a residential neighborhood within the Dutch town of Uithoorn in the province of North Holland.
-
E.
Namdalseid
Namdalseid is a former rural municipality in Trøndelag county, Norway, known for its forests, agriculture, and coastal landscape along the Namsenfjorden.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e131c608190b4ffdbada24a3208 |
completed | April 14, 2026, 12:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1cca84881909c7733bbc2609eea |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba6af4ed881908cb4b79cfa40977c |
completed | May 6, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba71a91fc8190b24185994673b33b |
completed | May 6, 2026, 8:39 p.m. |
Created at: April 9, 2026, 10:17 p.m.