Triple
T3698992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tideland |
E78527
|
entity |
| Predicate | protagonist |
P268
|
FINISHED |
| Object |
Jeliza-Rose
Jeliza-Rose is the imaginative and psychologically fragile young girl at the center of the dark fantasy film "Tideland," through whose perspective the story’s surreal and unsettling events unfold.
|
E380979
|
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: Jeliza-Rose | Statement: [Tideland, protagonist, Jeliza-Rose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeliza-Rose Context triple: [Tideland, protagonist, Jeliza-Rose]
-
A.
Julianna
Julianna is a feminine given name most notably borne by American actress Julianna Margulies.
-
B.
Lilia
Lilia is a feminine given name, often considered a variant of Lily and associated with the elegance and symbolism of the lily flower.
-
C.
Arielle
Arielle is a given name shared by various individuals, including Arielle Zuckerberg, a venture capitalist and younger sister of Meta co-founder Mark Zuckerberg.
-
D.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
E.
Madelyn
Madelyn is a feminine given name, often considered a modern variant of Madeline and commonly used in English-speaking countries.
- 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: Jeliza-Rose Triple: [Tideland, protagonist, Jeliza-Rose]
Generated description
Jeliza-Rose is the imaginative and psychologically fragile young girl at the center of the dark fantasy film "Tideland," through whose perspective the story’s surreal and unsettling events unfold.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeliza-Rose Target entity description: Jeliza-Rose is the imaginative and psychologically fragile young girl at the center of the dark fantasy film "Tideland," through whose perspective the story’s surreal and unsettling events unfold.
-
A.
Julianna
Julianna is a feminine given name most notably borne by American actress Julianna Margulies.
-
B.
Lilia
Lilia is a feminine given name, often considered a variant of Lily and associated with the elegance and symbolism of the lily flower.
-
C.
Arielle
Arielle is a given name shared by various individuals, including Arielle Zuckerberg, a venture capitalist and younger sister of Meta co-founder Mark Zuckerberg.
-
D.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
E.
Madelyn
Madelyn is a feminine given name, often considered a modern variant of Madeline and commonly used in English-speaking countries.
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc513d26c8190bfdf25f62af8c6ca |
completed | March 8, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3df86bc819088db92eecee69fd3 |
completed | March 14, 2026, 2:11 a.m. |
| NEDg | Description generation | batch_69b4c7e579d4819090edfa8a858c40c4 |
completed | March 14, 2026, 2:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4c86511e48190872b3d85019bc013 |
completed | March 14, 2026, 2:31 a.m. |
Created at: March 8, 2026, 3:26 p.m.