Triple
T5585232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Five studios |
E146738
|
entity |
| Predicate | legalEventEffect |
P6506
|
FINISHED |
| Object | end of vertical integration in Hollywood |
—
|
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: end of vertical integration in Hollywood | Statement: [Big Five studios, legalEventEffect, end of vertical integration in Hollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalEventEffect Context triple: [Big Five studios, legalEventEffect, end of vertical integration in Hollywood]
-
A.
eventEffect
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
-
B.
notableEffect
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
-
C.
hasLegalEffect
chosen
Indicates that an action, document, or condition produces recognized legal consequences or enforceable rights and obligations.
-
D.
eventInfluencedBy
Indicates that an event occurs or unfolds in a way that is causally or significantly affected by another entity, factor, or prior event.
-
E.
effectOnSchedule
Indicates how an event, action, or condition changes, disrupts, or influences a planned schedule or timeline.
- 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_69c0090287a08190b4098411effe970c |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02085d0e48190b8d185fe7f3d8579 |
completed | March 22, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69c01b16b9bc8190ab0b945507d90e05 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.