Triple
T312857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonard Bernstein |
E7643
|
entity |
| Predicate | awardReceived |
P11
|
FINISHED |
| Object | Tony Award |
E3842
|
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: Tony Award | Statement: [Leonard Bernstein, awardReceived, Tony Award]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Award Context triple: [Leonard Bernstein, awardReceived, Tony Award]
-
A.
Tony Award
chosen
The Tony Award is a prestigious American honor recognizing excellence in live Broadway theatre.
-
B.
Tony Award for Best Actor in a Play
The Tony Award for Best Actor in a Play is a prestigious annual honor presented for an outstanding leading performance by an actor in a Broadway play.
-
C.
Grammy Award for Best Musical Theater Album
The Grammy Award for Best Musical Theater Album is a prestigious music industry honor recognizing outstanding cast recordings of musical theater productions.
-
D.
Golden Globe Award
The Golden Globe Award is a major American accolade presented annually by the Hollywood Foreign Press Association to honor excellence in film and television.
-
E.
Emmy Award
The Emmy Award is a prestigious American television accolade recognizing excellence in various sectors of the television industry, including entertainment, news, and documentary programming.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea4aa16881909b2c8404b85992df |
completed | Feb. 28, 2026, 1:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3bc2963b48190b5bd7ac84c952486 |
completed | March 1, 2026, 4:10 a.m. |
Created at: Feb. 28, 2026, 1:07 p.m.