Triple
T13667520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Life in Film |
E327658
|
entity |
| Predicate | hasCharacters |
P111069
|
FINISHED |
| Object | aspiring filmmaker |
—
|
LITERAL FINISHED |
How this triple was built (2 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: aspiring filmmaker | Statement: [My Life in Film, hasCharacters, aspiring filmmaker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacters Context triple: [My Life in Film, hasCharacters, aspiring filmmaker]
-
A.
hasChildCharacters
Indicates that one entity includes or is associated with other entities that are considered its child characters in a hierarchical or narrative structure.
-
B.
usesCharactersAs
Indicates that one entity employs or incorporates specific characters (such as letters, symbols, or glyphs) from another entity for its representation or functioning.
-
C.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
D.
usesCharacter
Indicates that one entity employs, incorporates, or relies on a particular character (such as a symbol, letter, or persona) in its form, function, or representation.
-
E.
hasCharacterContext
Indicates that a character is associated with or participates in a particular contextual situation, setting, or state.
- F. None of above. chosen
Provenance (4 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65832688190aea688fee0a7cbdb |
completed | April 12, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69dbbe8d8d0881908d6e89954f44eed4 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc59ca1a88190a6abd3bd00554c93 |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 9, 2026, 9:52 p.m.