Triple
T78880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Zuckerberg |
E1581
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Mark |
E1581
|
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 Zuckerberg, hasGivenName, Mark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Context triple: [Mark Zuckerberg, hasGivenName, Mark]
-
A.
Mark
chosen
Mark is the given name of Mark Zuckerberg, the American technology entrepreneur and co-founder of Facebook.
-
B.
Andrew
Andrew is a masculine given name of Greek origin meaning "manly" or "brave," widely used in English-speaking countries and beyond.
-
C.
John
John H. Sununu is an American politician and engineer who served as Governor of New Hampshire and later as White House Chief of Staff under President George H. W. Bush.
-
D.
Gavin
Gavin is a masculine given name of Celtic origin, commonly used in English-speaking countries.
-
E.
Samuel
Samuel is the birth name of the famed American author Mark Twain, known for classics like "The Adventures of Tom Sawyer" and "Adventures of Huckleberry Finn."
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24f335b5c8190bf2158d884890ac2 |
completed | Feb. 28, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a26241d4c08190885dab6aef75dcf3 |
completed | Feb. 28, 2026, 3:34 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.