Triple
T5855781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marcel Hossa |
E130148
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marcel |
E201405
|
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: Marcel | Statement: [Marcel Hossa, givenName, Marcel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marcel Context triple: [Marcel Hossa, givenName, Marcel]
-
A.
Marcel
chosen
Marcel is a masculine given name of French origin, commonly used in various European countries.
-
B.
Maurice
Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
-
C.
Maurice
Maurice is a 1987 British romantic drama film, based on E.M. Forster’s novel, that explores same-sex love and class in early 20th-century England.
-
D.
Michel
Michel is a fictional character appearing in Frederick Forsyth’s political thriller novel "The Dogs of War."
-
E.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03555cd6081908b0307decd60d2bc |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a1bc58d081908568294278cbf3a9 |
completed | March 23, 2026, 2:13 a.m. |
Created at: March 22, 2026, 3:55 p.m.