Triple
T4459802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Police Story 2013 |
E98222
|
entity |
| Predicate | leadCharacterAgeDescriptor |
P37051
|
FINISHED |
| Object | middle-aged |
—
|
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: middle-aged | Statement: [Police Story 2013, leadCharacterAgeDescriptor, middle-aged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadCharacterAgeDescriptor Context triple: [Police Story 2013, leadCharacterAgeDescriptor, middle-aged]
-
A.
protagonistAge
chosen
Indicates the age of the main character or central figure in a narrative or scenario.
-
B.
leadCharacterStatus
Indicates the role or condition of an entity when it serves as the primary or central character in a narrative or context.
-
C.
portraysAgeGroup
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
D.
youngerVersionPortrayedBy
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
-
E.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
- F. None of above.
Provenance (3 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567184f481908a2787e4ac9bb345 |
completed | March 13, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69b34f649df081909d3cc2f6a1b8f282 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:33 p.m.