Triple
T13091140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loebner Prize |
E310465
|
entity |
| Predicate | organizer |
P123
|
FINISHED |
| Object | Hugh Loebner |
E1019445
|
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: Hugh Loebner | Statement: [Loebner Prize, organizer, Hugh Loebner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hugh Loebner Context triple: [Loebner Prize, organizer, Hugh Loebner]
-
A.
Hugh Loebner
chosen
Hugh Loebner was an American inventor and philanthropist best known for founding and sponsoring the Loebner Prize, an early annual competition in artificial intelligence based on the Turing Test.
-
B.
Moran Rosenblatt
Moran Rosenblatt is an Israeli actress known for her roles in film and television, including the acclaimed series "Tehran."
-
C.
Colin Allen
Colin Allen is an author known for his work on the book "Medicine Jar."
-
D.
Daniel G. Bobrow
Daniel G. Bobrow was an influential American computer scientist and early artificial intelligence researcher known for his work on natural language understanding and AI programming systems.
-
E.
Michael L. Littman
Michael L. Littman is an American computer scientist and professor known for his influential research in reinforcement learning, machine learning, and artificial intelligence.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813acbac8190b2fe5e07287457cf |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e27417308190b388be4a31ce4b5d |
completed | May 3, 2026, 5:51 a.m. |
Created at: April 9, 2026, 9:03 p.m.