Triple
T17694014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hindsight Experience Replay |
E441111
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | HER |
—
|
NE NERFINISHED |
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: [Hindsight Experience Replay, abbreviation, HER]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HER Context triple: [Hindsight Experience Replay, abbreviation, HER]
-
A.
HER
HER is the commonly used abbreviation for the Harvard Educational Review, a scholarly journal focused on education research and policy.
-
B.
HER
HER is the official herbarium code assigned to the Berggarten botanical collection, used in scientific and taxonomic references.
-
C.
HER
chosen
HER is a reinforcement learning technique that improves learning from sparse rewards by reinterpreting failed experiences as successful ones for alternative goals.
-
D.
HER
HER is the vehicle registration code assigned to the town of Herne in the German state of North Rhine-Westphalia.
-
E.
HER
H.E.R. is an American singer-songwriter and multi-instrumentalist known for her soulful R&B music, emotive vocals, and Grammy-winning work.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4715485d88190b9b6f347ff85d7c7 |
completed | April 19, 2026, 6:08 a.m. |
Created at: April 10, 2026, 10:04 a.m.