Triple
T16704259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian de Deux-Ponts |
E405923
|
entity |
| Predicate | serviceStartContext |
P124301
|
FINISHED |
| Object | served France during the American Revolutionary War |
—
|
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: served France during the American Revolutionary War | Statement: [Christian de Deux-Ponts, serviceStartContext, served France during the American Revolutionary War]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceStartContext Context triple: [Christian de Deux-Ponts, serviceStartContext, served France during the American Revolutionary War]
-
A.
serviceEntryContext
Indicates the contextual circumstances or environment in which a particular service entry is created, recorded, or applied.
-
B.
serviceStartLocation
Indicates the place where a service or operation is initiated or begins.
-
C.
activityStartContext
Indicates the circumstances, conditions, or situation present at the moment an activity begins.
-
D.
operationStart
Indicates the point in time or event at which a specific operation or process begins.
-
E.
campaignStartContext
Indicates the circumstances, conditions, or triggering situation under which a campaign is initiated.
- 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_69d8838db21081909589220fd71440a4 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3833496dc8190ae4b4a03ba04d69d |
completed | April 18, 2026, 1:12 p.m. |
| PD | Predicate disambiguation | batch_69e319c379f88190ac0adf812486f598 |
completed | April 18, 2026, 5:42 a.m. |
| PDg | Predicate description generation | batch_69e326b9e84881909a9166e65bd850d6 |
completed | April 18, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:19 a.m.