Triple
T3393901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Gastineau |
E71482
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mark |
E161211
|
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: Mark | Statement: [Mark Gastineau, givenName, Mark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Context triple: [Mark Gastineau, givenName, Mark]
-
A.
Mark
Mark is the given name of Mark Zuckerberg, the American technology entrepreneur and co-founder of Facebook.
-
B.
Mark
Mark is a river in the southern Netherlands and northern Belgium that flows through the province of North Brabant before joining the Dintel.
-
C.
Mark
Mark is a punctuation symbol used in writing systems, including those that employ the Cyrillic Extended-B Unicode block.
-
D.
Mark
Mark is one of the four canonical Gospels in the New Testament, traditionally attributed to John Mark and known for its concise, fast-paced account of the life, ministry, death, and resurrection of Jesus Christ.
-
E.
Mark
chosen
Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
- 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_69ad85a9c4a88190a854019341cb3b60 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb853746c8190bfa1447e6ebbefb3 |
completed | March 8, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b360aded3c8190ab4ca37b4aead1df |
completed | March 13, 2026, 12:56 a.m. |
Created at: March 8, 2026, 3:14 p.m.