Triple
T8315903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | It (Stephen King) |
E194705
|
entity |
| Predicate | enemy |
P4567
|
FINISHED |
| Object | Stan Uris |
E192773
|
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: Stan Uris | Statement: [It (Stephen King), enemy, Stan Uris]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stan Uris Context triple: [It (Stephen King), enemy, Stan Uris]
-
A.
Stan Uris
chosen
Stan Uris is a character from Stephen King’s horror novel "It," known as the most rational and skeptical member of the Losers' Club whose struggle with fear and faith deeply shapes the story’s emotional impact.
-
B.
Stanley Uris
Stanley Uris is a cautious, anxiety-prone member of the Losers' Club in Stephen King's "It," whose fear and vulnerability play a pivotal role in the story's exploration of trauma and courage.
-
C.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
D.
Stan Salfas
Stan Salfas is a film editor known for his work on major feature films, including the science fiction sequel "Dawn of the Planet of the Apes."
-
E.
Tom Schaul
Tom Schaul is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Dueling DQN architecture.
- 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_69ca82e6e2648190a31eaf6f4f757b2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f540b2081908ccb1b2ed040c74e |
completed | March 31, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd9583fa8081909778288f4c96de72 |
completed | April 1, 2026, 10 p.m. |
Created at: March 30, 2026, 5:55 p.m.