Triple
T14751751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Bolger as Emily Thomas |
E346624
|
entity |
| Predicate | hasPastTiesTo |
P7843
|
FINISHED |
| Object | cartel world |
—
|
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: cartel world | Statement: [Sarah Bolger as Emily Thomas, hasPastTiesTo, cartel world]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPastTiesTo Context triple: [Sarah Bolger as Emily Thomas, hasPastTiesTo, cartel world]
-
A.
hasHistoricalTieTo
chosen
Indicates a relationship where one entity is historically connected or linked to another through past events, associations, or influences.
-
B.
hasStrongTiesTo
Indicates a close, influential, and enduring relationship or connection exists between the referenced entities.
-
C.
hasHistoryIn
Indicates that an entity has a past involvement, presence, or record of activity within a particular domain, context, or location.
-
D.
hasNegativeHistoricalAssociation
Indicates that one entity is historically linked to another in a way that is viewed as harmful, problematic, or disreputable.
-
E.
hasTourHistoryWith
Indicates that two entities have previously participated together in one or more tours or touring events.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d40efc8190bb1be34c19a2b57c |
completed | April 14, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69de8bf9331481909582045cd567d91f |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:30 a.m.