Triple
T29890517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murder, She Said |
E759136
|
entity |
| Predicate | hasProtagonistDemographic |
P7875
|
FINISHED |
| Object | elderly woman |
—
|
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: elderly woman | Statement: [Murder, She Said, hasProtagonistDemographic, elderly woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProtagonistDemographic Context triple: [Murder, She Said, hasProtagonistDemographic, elderly woman]
-
A.
hasDemographic
chosen
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
B.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
C.
hasFictionalDemographic
Indicates that an entity is associated with a demographic group that is fictional or exists only within a created narrative or imagined context.
-
D.
hasChildProtagonist
Indicates that the work features a child as its main or central character.
-
E.
protagonistEthnicity
Indicates the ethnic background or cultural heritage associated with a work’s main character.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69ffab5adf2c819084700c5ea34615bf |
completed | May 9, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69ffaabffa208190b5214ca17cc8a5ea |
completed | May 9, 2026, 9:44 p.m. |
Created at: April 29, 2026, 6:02 p.m.