Triple
T38156638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brooke Wyndham |
E952903
|
entity |
| Predicate | hasLegalStatusInStory |
P2250
|
FINISHED |
| Object | accused of murder |
—
|
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: accused of murder | Statement: [Brooke Wyndham, hasLegalStatusInStory, accused of murder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalStatusInStory Context triple: [Brooke Wyndham, hasLegalStatusInStory, accused of murder]
-
A.
hasLegalStatus
chosen
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
B.
hasLegalStatusOver
Indicates that one entity holds a recognized legal authority, jurisdiction, or standing in relation to another entity.
-
C.
hasImaginedLegalStatus
Indicates that an entity is regarded or treated as having a particular legal status based on imagination, assumption, or fiction rather than an officially recognized legal designation.
-
D.
hasLegalStatusInSaudiLaw
Indicates that an entity possesses a specific legal status or recognition under the laws and regulations of Saudi Arabia.
-
E.
hasNoLegalStatus
Indicates that the referenced entity lacks any formally recognized legal standing, rights, or status under the applicable legal system.
- 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_69f76f0a67f4819080c492f61d688fcc |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69feb5e66224819083b87c3707a5a5e0 |
completed | May 9, 2026, 4:19 a.m. |
| PD | Predicate disambiguation | batch_69feb3bd700c8190991ed200cd3c04db |
completed | May 9, 2026, 4:10 a.m. |
Created at: May 3, 2026, 4:21 p.m.