Triple
T15575436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Lampert |
E374356
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Lampert |
E374356
|
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: Lampert | Statement: [Edward Lampert, hasSurname, Lampert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lampert Context triple: [Edward Lampert, hasSurname, Lampert]
-
A.
Lampert
chosen
Lampert is a surname most notably associated with American billionaire investor and former Sears Holdings CEO Edward Lampert.
-
B.
Lambert
Lambert is a masculine given name of Germanic origin, historically borne by various saints, nobles, and notable figures in Europe.
-
C.
Lampson
Lampson is a surname most notably associated with American politician Nick Lampson, a former U.S. Representative from Texas.
-
D.
Lampwick
Lampwick is a mischievous, troublemaking boy in the story of Pinocchio who leads the title character astray, ultimately transforming into a donkey as a consequence of his bad behavior.
-
E.
Lampa
Lampa is a commune and town in central Chile known for its semi-rural character and growing residential and industrial development near Santiago.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e2140388190a8df7b835eaa72ce |
completed | April 16, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4978ec8190a57de5d9a2ec6653 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:10 a.m.