Triple
T19454250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Family Video (Hawkins) |
E486694
|
entity |
| Predicate | targetCustomersInFiction |
P91717
|
FINISHED |
| Object | residents of Hawkins |
—
|
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: residents of Hawkins | Statement: [Family Video (Hawkins), targetCustomersInFiction, residents of Hawkins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetCustomersInFiction Context triple: [Family Video (Hawkins), targetCustomersInFiction, residents of Hawkins]
-
A.
targetInFiction
Indicates that one entity is the target or subject of an action, focus, or effect within a fictional work or narrative context.
-
B.
hasCustomerBaseInFiction
chosen
Indicates that an entity’s primary or significant group of customers exists within fictional works or fictional contexts.
-
C.
leadsToFictional
Indicates that one entity causes, results in, or gives rise to a fictional work, scenario, or construct involving another entity.
-
D.
targetsCharacter
Indicates that one entity is the intended focus or target of another entity’s action, effect, or behavior.
-
E.
createsInFiction
Indicates that one entity is the creator or originator of another entity within a fictional or narrative context.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c117ac8190a38c01c3191beaea |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.