Triple
T4041484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lou Henry Hoover |
E83959
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lou |
E38160
|
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: Lou | Statement: [Lou Henry Hoover, givenName, Lou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lou Context triple: [Lou Henry Hoover, givenName, Lou]
-
A.
Lou
chosen
Lou is a common diminutive form of the given name Louise.
-
B.
Lou
Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
-
C.
Luc
Luc is the given name of Luc Longley, the Australian former professional basketball player and three-time NBA champion with the Chicago Bulls.
-
D.
Lee
Lee is a residential district in southeast London known for its suburban character, green spaces, and Victorian and Edwardian housing.
-
E.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb3a9314819095dcf47675eedb48 |
completed | March 9, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5564d0fb881909ba645714be27b95 |
completed | March 14, 2026, 12:36 p.m. |
Created at: March 9, 2026, 3:37 p.m.