Triple
T30833000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Picket Fences |
E785273
|
entity |
| Predicate | hasMedicalDramaElements |
P195397
|
FINISHED |
| Object | Yes |
—
|
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: Yes | Statement: [Picket Fences, hasMedicalDramaElements, Yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalDramaElements Context triple: [Picket Fences, hasMedicalDramaElements, Yes]
-
A.
hasDramaticElements
Indicates that something contains features or qualities characteristic of drama, such as heightened emotion, tension, or conflict.
-
B.
hasFictionalEmergencyRoom
Indicates that an entity includes or features a fictional emergency room as part of its setting or content.
-
C.
hasDramaticMedium
Indicates a relationship where a dramatic work is associated with the medium or form through which it is expressed or presented.
-
D.
hasDramaticProduction
Indicates that an entity is associated with or features a specific dramatic production (such as a play, performance, or staged work).
-
E.
hasSeriesDoctor
Indicates that a particular doctor is associated with or responsible for a given series (such as a TV show, book series, or medical series).
- 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_69f224b73d8c81908129383bfb397c87 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
| PDg | Predicate description generation | batch_69fdd07724f88190a33ec602642d2ea3 |
completed | May 8, 2026, noon |
Created at: April 29, 2026, 8:44 p.m.