Triple
T12340281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayward Pines |
E294203
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Storyland
Storyland is a film and television production company known for producing the mystery-thriller series "Wayward Pines."
|
E978815
|
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: Storyland | Statement: [Wayward Pines, productionCompany, Storyland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Storyland Context triple: [Wayward Pines, productionCompany, Storyland]
-
A.
Neverland
Neverland is a magical, timeless island in J.M. Barrie’s Peter Pan stories, inhabited by children who never grow up, fairies, pirates, and fantastical creatures.
-
B.
Sky Land
Sky Land is a high-altitude, cloud-filled world in Super Mario Bros. 3 known for its vertical level design and airborne challenges.
-
C.
Wonderland
Wonderland is a 1999 British drama film directed by Michael Winterbottom that interweaves the lives of several Londoners over a Guy Fawkes Night weekend.
-
D.
Wonderland
Wonderland is a rapid transit station in Revere, Massachusetts, serving as the northern terminus of Boston’s MBTA Blue Line.
-
E.
Wonderland
Wonderland is a song by the British pop group Take That, known for its upbeat, anthemic style and inclusion on their 2017 album of the same name.
- 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: Storyland Triple: [Wayward Pines, productionCompany, Storyland]
Generated description
Storyland is a film and television production company known for producing the mystery-thriller series "Wayward Pines."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Storyland Target entity description: Storyland is a film and television production company known for producing the mystery-thriller series "Wayward Pines."
-
A.
Neverland
Neverland is a magical, timeless island in J.M. Barrie’s Peter Pan stories, inhabited by children who never grow up, fairies, pirates, and fantastical creatures.
-
B.
Sky Land
Sky Land is a high-altitude, cloud-filled world in Super Mario Bros. 3 known for its vertical level design and airborne challenges.
-
C.
Wonderland
Wonderland is a 1999 British drama film directed by Michael Winterbottom that interweaves the lives of several Londoners over a Guy Fawkes Night weekend.
-
D.
Wonderland
Wonderland is a rapid transit station in Revere, Massachusetts, serving as the northern terminus of Boston’s MBTA Blue Line.
-
E.
Wonderland
Wonderland is a song by the British pop group Take That, known for its upbeat, anthemic style and inclusion on their 2017 album of the same name.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7758dc8190bbc6a9ad00b01dce |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62aaa1d548190be065412aab70385 |
completed | May 2, 2026, 4:47 p.m. |
| NEDg | Description generation | batch_69f62c55aacc8190a0544306825bdfab |
completed | May 2, 2026, 4:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62d51ab8081909c6f534051019dca |
completed | May 2, 2026, 4:58 p.m. |
Created at: April 8, 2026, 9:53 p.m.