Triple
T6788673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Justice for All with Judge Cristina Perez |
E155876
|
entity |
| Predicate | hasReenactments |
P72998
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Justice for All with Judge Cristina Perez, hasReenactments, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReenactments Context triple: [Justice for All with Judge Cristina Perez, hasReenactments, true]
-
A.
hasRevivalsIn
Indicates that something has been brought back, renewed, or reintroduced in specific times, places, or contexts.
-
B.
hasReprise
Indicates that an action, theme, or element is repeated or returns after its initial occurrence.
-
C.
hasRemake
Indicates that one work is a new version or recreation of an earlier existing work.
-
D.
hasFlashbackStorylines
Indicates that the narrative includes scenes or sequences set in earlier time periods that reveal past events related to the main storyline.
-
E.
laterRecordedIn
Indicates that the referenced information or event was documented or captured at a later time in the specified source or record.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2aa2e0c8190b994261826ae001d |
completed | March 27, 2026, 6:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d0979ce0819094678896da4e3169 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1b0d2a48190b249dcc671b9b5e4 |
completed | March 27, 2026, 6:51 p.m. |
Created at: March 27, 2026, 2:14 p.m.