Triple
T5787662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lena Horne |
E128308
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lena |
E200105
|
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: Lena | Statement: [Lena Horne, givenName, Lena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lena Context triple: [Lena Horne, givenName, Lena]
-
A.
Lena
Lena is an alternate given name of Lee Krasner, the influential American abstract expressionist painter and wife of Jackson Pollock.
-
B.
Lena
chosen
Lena is a common feminine given name used in many languages, often derived from longer names such as Magdalena or Helena.
-
C.
Lena Raine
Lena Raine is a composer and producer best known for her atmospheric and emotional video game soundtracks, including work on titles like Celeste and Minecraft.
-
D.
Lana
Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
-
E.
Lena Grove
Lena Grove is a central character in William Faulkner's novel "Light in August," known for her determined journey to Jefferson, Mississippi while pregnant and searching for the father of her child.
- 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_69c0084450048190bc647b649a05136b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a5297c88190bd28adb2552a26f4 |
completed | March 22, 2026, 5:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0981a002c8190ac8ed7407a80919c |
completed | March 23, 2026, 1:32 a.m. |
Created at: March 22, 2026, 3:51 p.m.