Triple
T16932291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Drexel |
E410739
|
entity |
| Predicate | characterInMedium |
P12208
|
FINISHED |
| Object | Broadway play |
—
|
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: Broadway play | Statement: [Norman Drexel, characterInMedium, Broadway play]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterInMedium Context triple: [Norman Drexel, characterInMedium, Broadway play]
-
A.
characterIn
chosen
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
B.
characterInFocus
Indicates that a particular character is the primary subject or focal point within a given context, scene, or narrative segment.
-
C.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
D.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
E.
character3
Indicates a tertiary or additional character role associated with an entity, typically the third distinct character linked within a given context or work.
- F. None of above.
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_69d886c886688190967be07322597ac9 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cf2650e08190b669d0cf2cf1275b |
completed | April 18, 2026, 6:36 p.m. |
| PD | Predicate disambiguation | batch_69e32b982f548190b08414d55810de19 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:30 a.m.