Triple
T20688444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa Loring |
E508484
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object |
Blood Frenzy
Blood Frenzy is a 1987 low-budget American slasher film known for its desert setting and cult status among horror fans.
|
E1446257
|
NE FINISHED |
How this triple was built (4 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: Blood Frenzy | Statement: [Lisa Loring, appearedIn, Blood Frenzy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blood Frenzy Context triple: [Lisa Loring, appearedIn, Blood Frenzy]
-
A.
Bloodlust
Bloodlust is a studio album by American thrash metal band Body Count, known for its aggressive sound and politically charged lyrics.
-
B.
Frenzy
"Frenzy" is a popular Nigerian Afropop song by artist D'Prince, known for its energetic beat and club-friendly vibe.
-
C.
Frenzy
Frenzy is a Hearthstone keyword that triggers a special effect the first time a minion survives damage.
-
D.
Frenzy
Frenzy is a small, hyperactive Decepticon known for his disruptive tactics, espionage skills, and penchant for causing chaos in the Transformers universe.
-
E.
Frenzy
Frenzy is a 1972 British thriller film directed by Alfred Hitchcock, known for its dark humor and disturbing portrayal of a serial killer in London.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Blood Frenzy Triple: [Lisa Loring, appearedIn, Blood Frenzy]
Generated description
Blood Frenzy is a 1987 low-budget American slasher film known for its desert setting and cult status among horror fans.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Blood Frenzy Target entity description: Blood Frenzy is a 1987 low-budget American slasher film known for its desert setting and cult status among horror fans.
-
A.
Bloodlust
Bloodlust is a studio album by American thrash metal band Body Count, known for its aggressive sound and politically charged lyrics.
-
B.
Frenzy
"Frenzy" is a popular Nigerian Afropop song by artist D'Prince, known for its energetic beat and club-friendly vibe.
-
C.
Frenzy
Frenzy is a Hearthstone keyword that triggers a special effect the first time a minion survives damage.
-
D.
Frenzy
Frenzy is a small, hyperactive Decepticon known for his disruptive tactics, espionage skills, and penchant for causing chaos in the Transformers universe.
-
E.
Frenzy
Frenzy is a 1972 British thriller film directed by Alfred Hitchcock, known for its dark humor and disturbing portrayal of a serial killer in London.
- F. None of above. chosen
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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c10b7b808190bdb8b08e53168fb8 |
completed | April 21, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08d7e04b7c8190b49f638fb8ff88c6 |
completed | May 16, 2026, 8:47 p.m. |
| NEDg | Description generation | batch_6a08dbdeb8e48190b8fda90fbd2a1837 |
completed | May 16, 2026, 9:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08dc66bc4081908bdcc559fc777cd3 |
completed | May 16, 2026, 9:06 p.m. |
Created at: April 16, 2026, 11:49 a.m.