Triple
T13504008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Schröder |
E320964
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Martin |
E223140
|
NE 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: Martin | Statement: [Martin Schröder, givenName, Martin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Context triple: [Martin Schröder, givenName, Martin]
-
A.
Martin
Martin is the middle name of Henry Martin Tupper, an individual likely known in historical or biographical records.
-
B.
Martin
Martin is the given name of Klaus Martin Einstein, the son of physicist Hans Albert Einstein and grandson of Albert Einstein.
-
C.
Martin
chosen
Martin is a masculine given name of Latin origin, commonly used in many European languages.
-
D.
Martin
Martin is the given name of Martin Luther the Younger, a 16th-century German theologian and the son of Protestant Reformation leader Martin Luther.
-
E.
Martin
Martin is a city in central Slovakia known as a historical center of Slovak culture and national revival.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf810e248190a060481004503f96 |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7548bc188819090773db66b0103a1 |
completed | May 3, 2026, 1:58 p.m. |
Created at: April 9, 2026, 9:43 p.m.