Triple
T34108361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SNCF Grand Voyageur |
E874768
|
entity |
| Predicate | loyaltyMetric |
P178417
|
FINISHED |
| Object | travel frequency |
—
|
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: travel frequency | Statement: [SNCF Grand Voyageur, loyaltyMetric, travel frequency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loyaltyMetric Context triple: [SNCF Grand Voyageur, loyaltyMetric, travel frequency]
-
A.
loyaltyDimension
Indicates the degree or aspect of loyalty characterizing the relationship between entities.
-
B.
loyaltyMechanism
Indicates a mechanism or process through which loyalty is established, maintained, or reinforced between entities.
-
C.
loyaltyArc
Indicates a character’s progression in loyalty toward another entity, typically showing how trust, allegiance, or faithfulness strengthens, weakens, or transforms over time.
-
D.
loyaltySymbolizedBy
Indicates that an instance of loyalty is represented or expressed by a particular symbol or emblem.
-
E.
loyaltyGoal
Indicates that one entity has the objective or commitment to remain faithful, supportive, or devoted to another entity or cause.
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7107acf0481909b01467b9ebbde01 |
completed | May 3, 2026, 9:08 a.m. |
| PD | Predicate disambiguation | batch_69f70f3a54d481909ba6bdda3647b761 |
completed | May 3, 2026, 9:02 a.m. |
| PDg | Predicate description generation | batch_69f70fb41a9c8190a121e62e510dc18a |
completed | May 3, 2026, 9:04 a.m. |
Created at: May 1, 2026, 1:53 a.m.