Triple
T16097085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miller-Milkis Productions |
E390514
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
Miller-Milkis
Miller-Milkis is a television production company best known for producing popular American TV series in the 1970s and 1980s.
|
E1193996
|
NE FINISHED |
How this triple was built (4 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: Miller-Milkis | Statement: [Miller-Milkis Productions, abbreviation, Miller-Milkis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miller-Milkis Context triple: [Miller-Milkis Productions, abbreviation, Miller-Milkis]
-
A.
Milchick
Milchick is a key character in the psychological sci-fi series "Severance," serving as a devoted and unsettling middle-management figure overseeing the severed employees at Lumon Industries.
-
B.
Millerovo
Millerovo is a town in southwestern Russia known as a local administrative and transport center within Rostov Oblast.
-
C.
Mullins
Mullins is an English-language surname of Irish and Norman origin borne by various notable individuals across history.
-
D.
Musselman
Musselman is a surname most notably associated with American basketball coaches Bill Musselman and his son Eric Musselman.
-
E.
Milak
Milak is a border town in India’s Uttar Pradesh state, known as a road-linked crossing point on the route to Afghanistan via Zaranj.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Miller-Milkis Triple: [Miller-Milkis Productions, abbreviation, Miller-Milkis]
Generated description
Miller-Milkis is a television production company best known for producing popular American TV series in the 1970s and 1980s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Miller-Milkis Target entity description: Miller-Milkis is a television production company best known for producing popular American TV series in the 1970s and 1980s.
-
A.
Milchick
Milchick is a key character in the psychological sci-fi series "Severance," serving as a devoted and unsettling middle-management figure overseeing the severed employees at Lumon Industries.
-
B.
Millerovo
Millerovo is a town in southwestern Russia known as a local administrative and transport center within Rostov Oblast.
-
C.
Mullins
Mullins is an English-language surname of Irish and Norman origin borne by various notable individuals across history.
-
D.
Musselman
Musselman is a surname most notably associated with American basketball coaches Bill Musselman and his son Eric Musselman.
-
E.
Milak
Milak is a border town in India’s Uttar Pradesh state, known as a road-linked crossing point on the route to Afghanistan via Zaranj.
- F. None of above. chosen
Provenance (5 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e18593d0fc8190aa3ba3edb4219aaa |
completed | April 17, 2026, 12:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb991ba88190ad568a49069f9701 |
completed | May 10, 2026, 2:21 a.m. |
| NEDg | Description generation | batch_69ffec4898088190bed531e33418c7e5 |
completed | May 10, 2026, 2:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffecce96508190a53f100e3207ebac |
completed | May 10, 2026, 2:26 a.m. |
Created at: April 10, 2026, 4:59 a.m.