Triple
T20622964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Famous Monsters |
E506746
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Them |
—
|
NE NERFINISHED |
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: Them | Statement: [Famous Monsters, hasTrack, Them]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Them Context triple: [Famous Monsters, hasTrack, Them]
-
A.
Them
Them is a 1969 novel by Joyce Carol Oates that portrays the struggles of a working-class Detroit family amid urban violence and social upheaval.
-
B.
Them
Them was a 1960s Northern Irish rock band fronted by Van Morrison, best known for pioneering garage rock with songs like "Gloria."
-
C.
Them!
chosen
Them! is a 1954 science fiction horror film about giant, irradiated ants that became a classic of the atomic-age monster movie genre.
-
D.
Monster
Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6abe3177c8190ad1b2ca8b1e0a560 |
completed | April 20, 2026, 10:42 p.m. |
Created at: April 16, 2026, 11:42 a.m.