Triple
T37725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broadway Theatre District |
E746
|
entity |
| Predicate | typicalProductionType |
P2475
|
FINISHED |
| Object | musical |
—
|
LITERAL 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: musical | Statement: [Broadway Theatre District, typicalProductionType, musical]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProductionType Context triple: [Broadway Theatre District, typicalProductionType, musical]
-
A.
produces
Indicates that one entity creates, generates, or yields another entity as a result or output.
-
B.
standardType
Indicates that one entity is classified as the standard, canonical, or reference type for another entity or context.
-
C.
typicalEngine
Indicates that an entity is the standard or commonly used engine for another entity (such as a vehicle, device, or system).
-
D.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
E.
typicalKey
Indicates that the referenced key is the standard or most commonly used key associated with an entity or context.
- F. None of above. chosen
Provenance (4 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24b4d5bd08190a3a48eb26e67768c |
completed | Feb. 28, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a24ab6141881908701106aa97e4735 |
completed | Feb. 28, 2026, 1:53 a.m. |
| PDg | Predicate description generation | batch_69a24b4c59b08190854b5335f5eff790 |
completed | Feb. 28, 2026, 1:56 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.