Triple
T1023108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aurel Stein |
E22082
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marc |
E48930
|
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: Marc | Statement: [Aurel Stein, givenName, Marc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marc Context triple: [Aurel Stein, givenName, Marc]
-
A.
Marc
chosen
Marc is the given name of Marc Andreessen, the influential American entrepreneur, software engineer, and venture capitalist known for co-creating the Mosaic web browser and co-founding Netscape and Andreessen Horowitz.
-
B.
Marcus
Marcus is a masculine given name of ancient Roman origin that has been widely used across many cultures and historical periods.
-
C.
Mark
Mark is the given name of Mark Zuckerberg, the American technology entrepreneur and co-founder of Facebook.
-
D.
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.
-
E.
Martin
Martin is the given name of Martin Luther King Jr., the prominent American civil rights leader and Baptist minister who advocated nonviolent resistance to racial segregation.
- 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_69a493d6e380819097b384986ffc315c |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7e0f8908190bfe0a4cd8b31dfed |
completed | March 1, 2026, 10:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bb83118819098a2b283a1cbf8d6 |
completed | March 7, 2026, 2:52 p.m. |
Created at: March 1, 2026, 7:41 p.m.