Triple
T10403183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Ursula |
E245197
|
entity |
| Predicate | companionsNumberTradition |
P45948
|
FINISHED |
| Object | 11,000 virgins |
—
|
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: 11,000 virgins | Statement: [Saint Ursula, companionsNumberTradition, 11,000 virgins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: companionsNumberTradition Context triple: [Saint Ursula, companionsNumberTradition, 11,000 virgins]
-
A.
hasNumberOfCompanions
chosen
Indicates the quantity of companions or associates that an entity has.
-
B.
companionsCategory
Indicates that one entity is classified under a specific category related to companions or companionship.
-
C.
numberOfTribes
Indicates the total count of distinct tribes associated with a given entity or context.
-
D.
wasCompanionOf
Indicates that one entity accompanied or associated closely with another, typically as a partner, ally, or fellow participant over some period of time.
-
E.
membersPerTribe
Indicates the number of individual members associated with each tribe in the relationship.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9e535d48190b8fc377df543e058 |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb438c481908dff87c47de2f069 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:08 p.m.