Triple
T15670641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Doyenne |
E377300
|
entity |
| Predicate | temporalConnotation |
P119686
|
FINISHED |
| Object | age and tradition |
—
|
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: age and tradition | Statement: [La Doyenne, temporalConnotation, age and tradition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalConnotation Context triple: [La Doyenne, temporalConnotation, age and tradition]
-
A.
temporal
Indicates a relationship that situates one event, state, or entity in time relative to another (e.g., before, after, or during).
-
B.
temporality
Indicates the time-related relationship between events or states, such as their order, duration, or simultaneity.
-
C.
temporalAspect
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
-
D.
temporalEffect
Indicates a relationship where one event, state, or action produces consequences or changes that occur at a later time.
-
E.
hasTemporalRole
Indicates that an entity participates in a role or function that is defined, constrained, or characterized by a specific time or temporal context.
- 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_69d85cd2e28481909d4e975bee20872f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04f1254508190a77a16b7bfd299ad |
completed | April 16, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69deda8b36a4819081cb5708fe77ef51 |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f3016c8190ac68d76e65e07af4 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:16 a.m.