Triple
T35118800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tysha |
E1014112
|
entity |
| Predicate | metWhile |
P8815
|
FINISHED |
| Object | Tyrion was traveling with Jaime Lannister |
—
|
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: Tyrion was traveling with Jaime Lannister | Statement: [Tysha, metWhile, Tyrion was traveling with Jaime Lannister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: metWhile Context triple: [Tysha, metWhile, Tyrion was traveling with Jaime Lannister]
-
A.
metThrough
Indicates that two entities became acquainted or connected as a result of an intermediary person, event, platform, or context through which they first met.
-
B.
metOn
Indicates that two or more entities encountered each other at the same time and place for the first time or for a particular meeting.
-
C.
metBetween
chosen
Indicates that two or more entities had an in-person or virtual meeting or encounter with each other during a specified time or context.
-
D.
meets
Indicates that two or more entities come together at the same place and time, typically for interaction or a shared purpose.
-
E.
metEnAvant
Indicates that one entity is highlighted, emphasized, or brought to the foreground in relation to another entity or context.
- 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_69f76dd8b6948190aaa32b081816bd94 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78ce78b508190955848e133398dc8 |
completed | May 3, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69f78b8f4cc08190b49fccd798cb25d7 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:01 p.m.