Triple
T15964788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt King |
E387157
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Hugh |
E20500
|
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: Hugh | Statement: [Matt King, relative, Hugh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hugh Context triple: [Matt King, relative, Hugh]
-
A.
Hugh
chosen
Hugh is a masculine given name of Germanic origin, commonly used in English-speaking countries.
-
B.
Henry
Henry is the given name of Henry A. Kissinger, the influential American diplomat and political scientist who served as U.S. Secretary of State and National Security Advisor.
-
C.
Henry
Henry is a common English surname borne by numerous notable individuals across politics, sports, and the arts.
-
D.
Henry
Henry is a masculine given name of Germanic origin that has been widely used by European royalty and notable historical figures.
-
E.
Henry
Henry is a character who serves as the narrator in the 1933 film "The Power and the Glory," providing the story’s perspective and framing its events.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1572475948190af0e3b12de0f6cea |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe84f4888190b3fdb5f32763f78d |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:54 a.m.