Triple
T10282352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clark Peterson |
E241132
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Monster |
E50475
|
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: Monster | Statement: [Clark Peterson, notableWork, Monster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monster Context triple: [Clark Peterson, notableWork, Monster]
-
A.
Monster
Monster is a town in the Dutch province of South Holland, known for its coastal location near the North Sea and its greenhouse horticulture.
-
B.
Monster
"Monster" is a critically acclaimed Japanese manga series by Naoki Urasawa, known for its dark psychological thriller narrative about a doctor entangled with a serial killer.
-
C.
Monster
chosen
Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
-
D.
Monster
"Monster" is a standout track from Kanye West’s critically acclaimed album *My Beautiful Dark Twisted Fantasy*, known for its high-profile guest verses and dark, aggressive themes.
-
E.
Monster
Monster is a popular energy drink brand known for its high-caffeine beverages and aggressive, extreme-sports-oriented marketing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2a177b48190aab7d7857f5bba7b |
completed | April 7, 2026, 9:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d012ae481909633b1333dc88b63 |
completed | April 9, 2026, 3:29 a.m. |
Created at: April 6, 2026, 11:39 a.m.