Triple
T19107267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Martin |
E467688
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Martin |
—
|
NE NERFINISHED |
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: [Michael Martin, familyName, Martin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Context triple: [Michael Martin, familyName, Martin]
-
A.
Martin
chosen
Martin is a masculine given name of Latin origin, commonly used in many European languages.
-
B.
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.
-
C.
Martin
Martin is a city in central Slovakia known as a historical center of Slovak culture and national revival.
-
D.
Martin
Martin is a 1978 American horror film directed by George A. Romero that blends psychological drama and vampire mythology in a gritty, realistic style.
-
E.
Martin
Martin is the middle name of Henry Martin Tupper, an individual likely known in historical or biographical records.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e391245c8190b1393577b61c4f76 |
completed | April 20, 2026, 8:28 a.m. |
Created at: April 10, 2026, 12:04 p.m.