Triple
T494722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Martin |
E10266
|
entity |
| Predicate | hasLegend |
P1582
|
FINISHED |
| Object | cutting his military cloak in half to share with a freezing beggar |
—
|
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: cutting his military cloak in half to share with a freezing beggar | Statement: [St Martin, hasLegend, cutting his military cloak in half to share with a freezing beggar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegend Context triple: [St Martin, hasLegend, cutting his military cloak in half to share with a freezing beggar]
-
A.
hasLegendAssociatedWith
chosen
Indicates that something is connected to or accompanied by a traditional story, myth, or legend.
-
B.
legend
Indicates that an entity is a traditional or historical story, figure, or narrative widely regarded as legendary rather than strictly factual.
-
C.
hasLegacy
Indicates that an entity leaves behind a lasting impact, influence, or inheritance that continues to exist or be recognized over time.
-
D.
hasLabel
Indicates that an entity is associated with a specific textual label or name used to identify or describe it.
-
E.
hasMarker
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f0fdd5608190815fa36485df8962 |
completed | Feb. 28, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69a2edf90ca88190b6a182e5b6733612 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.