Triple
T11945675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hattie Hardy Lott |
E284290
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lott |
E565236
|
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: Lott | Statement: [Hattie Hardy Lott, familyName, Lott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lott Context triple: [Hattie Hardy Lott, familyName, Lott]
-
A.
Lott
chosen
Lott is the surname of English singer and actress Pixie Lott, known for her pop hits and work in film and television.
-
B.
Lotter
Lotter is a German surname historically associated with printers and publishers active during the Reformation era.
-
C.
Luttig
Luttig is the surname of J. Michael Luttig, a prominent American conservative jurist and former federal appellate judge known for his influence on constitutional law and the judiciary.
-
D.
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.
-
E.
Lou
Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903456ec0819082b8b10755a6b732 |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f458cbb08881909a71f0592c9231ae |
completed | May 1, 2026, 7:39 a.m. |
Created at: April 8, 2026, 9:45 p.m.