Triple
T13189743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jaime Lannister |
E313950
|
entity |
| Predicate | associatedWords |
P10003
|
FINISHED |
| Object | A Lannister always pays his debts |
—
|
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: A Lannister always pays his debts | Statement: [Jaime Lannister, associatedWords, A Lannister always pays his debts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWords Context triple: [Jaime Lannister, associatedWords, A Lannister always pays his debts]
-
A.
associatedKeyword
Indicates that one entity is linked to or characterized by a particular keyword used for identification, categorization, or retrieval.
-
B.
associatedVow
Indicates a relationship where a vow is linked or connected to a particular entity or event.
-
C.
associatedWithVerb
Indicates that one entity is connected or linked to another through some verb-based relationship or action.
-
D.
linguisticallyRelatedTo
chosen
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
E.
termAlsoUsedFor
Indicates that one term is also used to refer to the same or closely related concept as another term.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc6bc108190b5a6a265bf6e9fd4 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:15 p.m.