Triple
T10653507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Hani |
E251030
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Martin
Martin is the given first name of South African anti-apartheid leader Chris Hani, whose full name was Martin Thembisile "Chris" Hani.
|
E877378
|
NE FINISHED |
How this triple was built (4 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: [Chris Hani, givenName, Martin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Context triple: [Chris Hani, 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 the central protagonist of the 1991 psychological thriller film "Proof," around whom the story’s exploration of trust, perception, and human connection revolves.
-
C.
Martin
Martin is a pessimistic scholar who serves as one of Candide’s key philosophical foils in Voltaire’s satirical novella "Candide."
-
D.
Martin
Martin was the first name of Martin Luther, a prominent Nazi official who served as a diplomat in the German Foreign Office during the Third Reich.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Martin Triple: [Chris Hani, givenName, Martin]
Generated description
Martin is the given first name of South African anti-apartheid leader Chris Hani, whose full name was Martin Thembisile "Chris" Hani.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Martin Target entity description: Martin is the given first name of South African anti-apartheid leader Chris Hani, whose full name was Martin Thembisile "Chris" Hani.
-
A.
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.
-
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 was the first name of Martin Luther, a prominent Nazi official who served as a diplomat in the German Foreign Office during the Third Reich.
-
D.
Martin
Martin is a masculine given name of Latin origin, commonly used in many European languages.
-
E.
Martin
Martin is a common surname of European origin, widely borne by individuals across many countries and cultures.
- F. None of above. chosen
Provenance (5 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dff85674819099bf40c9f4fede15 |
completed | April 8, 2026, 11:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d97a69a57c81908bf99cac0c0a49f2 |
completed | April 10, 2026, 10:32 p.m. |
| NEDg | Description generation | batch_69d97cc2b66c8190909a23927fbe3af5 |
completed | April 10, 2026, 10:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d97e13913081908dd1fb60fa44db05 |
completed | April 10, 2026, 10:47 p.m. |
Created at: April 8, 2026, 9:06 p.m.