Triple
T21436431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rails & Ties |
E528823
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Megan Stark
Megan Stark is a central character in the 2007 drama film "Rails & Ties," portrayed as a young girl whose life becomes intertwined with a troubled train engineer and his wife after a tragic accident.
|
E1522461
|
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: Megan Stark | Statement: [Rails & Ties, hasCharacter, Megan Stark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Stark Context triple: [Rails & Ties, hasCharacter, Megan Stark]
-
A.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
B.
Megan Burns
Megan Burns is a British actress best known for her role as Hannah in the post-apocalyptic horror film "28 Days Later."
-
C.
Megan Brock
Megan Brock is a character in John Grisham’s legal thriller "The Street Lawyer," involved in the novel’s exploration of homelessness, justice, and moral responsibility.
-
D.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
E.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
- 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: Megan Stark Triple: [Rails & Ties, hasCharacter, Megan Stark]
Generated description
Megan Stark is a central character in the 2007 drama film "Rails & Ties," portrayed as a young girl whose life becomes intertwined with a troubled train engineer and his wife after a tragic accident.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Megan Stark Target entity description: Megan Stark is a central character in the 2007 drama film "Rails & Ties," portrayed as a young girl whose life becomes intertwined with a troubled train engineer and his wife after a tragic accident.
-
A.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
B.
Megan Burns
Megan Burns is a British actress best known for her role as Hannah in the post-apocalyptic horror film "28 Days Later."
-
C.
Megan Brock
Megan Brock is a character in John Grisham’s legal thriller "The Street Lawyer," involved in the novel’s exploration of homelessness, justice, and moral responsibility.
-
D.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
E.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
- 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_69e0c4569fa081908101baa24f8745db |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b537f39081909220577618657805 |
completed | April 22, 2026, 11:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a9ea7fa78819086767bdd9bff499b |
completed | May 18, 2026, 5:07 a.m. |
| NEDg | Description generation | batch_6a0aa014aa908190ad7a9c9d76a6ab4e |
completed | May 18, 2026, 5:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0aa04788488190875bea3775c58e2d |
completed | May 18, 2026, 5:14 a.m. |
Created at: April 16, 2026, 6:02 p.m.