Triple
T15926925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahershalalhashbaz |
E386226
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | "Maher" |
E706069
|
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: "Maher" | Statement: [Mahershalalhashbaz, hasComponent, "Maher"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Maher" Context triple: [Mahershalalhashbaz, hasComponent, "Maher"]
-
A.
Maher
chosen
Maher is a masculine given name of Arabic origin commonly used in the Middle East and among Arabic-speaking communities worldwide.
-
B.
McHale
McHale is an Irish surname borne by various notable figures in sports, entertainment, and public life.
-
C.
Mulligan
Mulligan is a surname of Irish origin borne by various notable individuals, including the English actress Carey Mulligan.
-
D.
Mr Macey
Mr Macey is a kindly, somewhat eccentric village clerk and long-time resident of Candleford in "Lark Rise to Candleford," known for his nostalgic stories and gentle humor.
-
E.
The Mister
The Mister is a contemporary romance novel by E. L. James, known for its Cinderella-style love story and for being her follow-up to the Fifty Shades series.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156866de48190a744e8dcaa0c66f1 |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb5b0833081909668c042234b5b75 |
completed | May 9, 2026, 10:31 p.m. |
Created at: April 10, 2026, 4:52 a.m.