Triple
T6699011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marty Schottenheimer |
E152827
|
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: [Marty Schottenheimer, givenName, Martin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Context triple: [Marty Schottenheimer, givenName, Martin]
-
A.
Martin
Martin is a minor but kind-hearted character in Ernest Hemingway's novella "The Old Man and the Sea," known for helping the old fisherman Santiago.
-
B.
Martin
Martin is a common surname of European origin, widely borne by individuals across many countries and cultures.
-
C.
Martin
chosen
Martin is a masculine given name of Latin origin, commonly used in many European languages.
-
D.
Martin
Martin was the given name of Martin I of Aragon, a medieval king who ruled the Crown of Aragon at the turn of the 15th century.
-
E.
Martin
Martin is a character in Don DeLillo’s novel "Falling Man," which explores the personal and psychological aftermath of the September 11 attacks.
- 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d0a6082c819097a4301538399f59 |
completed | March 27, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f7bfcb048190b682f4ec7e404b3e |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:05 p.m.