Triple

T34922544
Position Surface form Disambiguated ID Type / Status
Subject Better Watch Out E1007184 entity
Predicate alsoKnownAs P39 FINISHED
Object Safe Neighborhood
Safe Neighborhood is an alternative title for the 2016 Australian-American black comedy horror film "Better Watch Out," which centers on a seemingly routine babysitting job that turns disturbingly violent during the holidays.
E2117056 NE 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: Safe Neighborhood | Statement: [Better Watch Out, alsoKnownAs, Safe Neighborhood]
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: Safe Neighborhood
Triple: [Better Watch Out, alsoKnownAs, Safe Neighborhood]
Generated description
Safe Neighborhood is an alternative title for the 2016 Australian-American black comedy horror film "Better Watch Out," which centers on a seemingly routine babysitting job that turns disturbingly violent during the holidays.

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_69f76dc2b6b0819095a61debbd405269 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7824dc3f0819092a5102895b4a478 completed May 3, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3786f9e55c8190a89f8a6a9a5753d1 completed June 21, 2026, 6:38 a.m.
NEDg Description generation batch_6a378f9e0e5881909de7792d091ddcdf completed June 21, 2026, 7:15 a.m.
NED2 Entity disambiguation (via description) batch_6a37906a102c8190a47f112103741b80 completed June 21, 2026, 7:19 a.m.
Created at: May 3, 2026, 4 p.m.