Triple
T4901248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ansel Adams |
E109802
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Michael Adams |
E109808
|
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: Michael Adams | Statement: [Ansel Adams, child, Michael Adams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Adams Context triple: [Ansel Adams, child, Michael Adams]
-
A.
Michael Adams
chosen
Michael Adams is a common personal name shared by numerous individuals across fields such as sports, politics, academia, and the arts.
-
B.
Vladimir Kramnik
Vladimir Kramnik is a Russian chess grandmaster and former World Chess Champion renowned for defeating Garry Kasparov in 2000 and for his deep strategic style.
-
C.
Vic Fischer
Vic Fischer is an American planner and politician best known as one of the key architects and delegates of Alaska’s state constitution.
-
D.
Veselin Topalov
Veselin Topalov is a Bulgarian chess grandmaster and former FIDE World Chess Champion known for his aggressive, dynamic playing style.
-
E.
Arthur Guez
Arthur Guez is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Double DQN algorithm.
- 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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e4dd6bc819094b1cbf533510995 |
completed | March 20, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6fd2a0348190a285ea1a62e7ae1b |
completed | March 21, 2026, 10:15 a.m. |
Created at: March 20, 2026, 1:28 p.m.