Triple
T10323505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoyte van Hoytema |
E242698
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Her |
E50437
|
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: Her | Statement: [Hoyte van Hoytema, notableWork, Her]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Her Context triple: [Hoyte van Hoytema, notableWork, Her]
-
A.
Her
"Her" is a lesser-known work by American poet, painter, and City Lights Books co-founder Lawrence Ferlinghetti, reflecting his characteristic Beat-influenced, avant-garde literary style.
-
B.
Her
chosen
Her is a 2013 science-fiction romantic drama film directed by Spike Jonze that explores a man's emotional relationship with an advanced artificial intelligence operating system.
-
C.
Her
"Her" is a soulful R&B song by American singer-songwriter SiR, known for its smooth production and introspective lyrics about love and vulnerability.
-
D.
HER
HER is the official herbarium code assigned to the Berggarten botanical collection, used in scientific and taxonomic references.
-
E.
HER
HER is a reinforcement learning technique that improves learning from sparse rewards by reinterpreting failed experiences as successful ones for alternative goals.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d6cdb6cc8190b37ca4494287128b |
completed | April 7, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71da2053481908fe5ed097b480cdd |
completed | April 9, 2026, 3:31 a.m. |
Created at: April 6, 2026, 11:50 a.m.