Triple
T23231165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Thomas Glahn |
E581156
|
entity |
| Predicate | hasRelationshipTypeWithEdvarda |
P151447
|
FINISHED |
| Object | turbulent love affair |
—
|
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: turbulent love affair | Statement: [Lieutenant Thomas Glahn, hasRelationshipTypeWithEdvarda, turbulent love affair]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithEdvarda Context triple: [Lieutenant Thomas Glahn, hasRelationshipTypeWithEdvarda, turbulent love affair]
-
A.
hasRelationshipTypeWith Valère
Indicates that an entity stands in a specific, characterized type of relationship with Valère.
-
B.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
C.
hasRelationshipTypeWithNenaDaconte
Indicates that there exists a specific type of relationship between an entity and Nena Daconte.
-
D.
hasRelationshipTypeWithOmar
Indicates that an entity stands in a specified type of interpersonal or associative relationship with Omar.
-
E.
hasRelationshipTypeWithBalducci
Indicates that there exists a specific, defined type of relationship between an entity and Balducci.
- F. None of above. chosen
Provenance (4 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_69e246043c48819089bae72c9a9c306c |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f19231ef908190a791b4967916a66f |
completed | April 29, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69effcdadec0819092ec1749ee453b4e |
completed | April 28, 2026, 12:18 a.m. |
| PDg | Predicate description generation | batch_69f01d8770d081908897c28b04e5faea |
completed | April 28, 2026, 2:37 a.m. |
Created at: April 17, 2026, 4:09 p.m.