Triple
T12544133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamanaka factors |
E299917
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Oct4
Oct4 is a key pluripotency transcription factor essential for maintaining and reprogramming stem cells to an undifferentiated state.
|
E989195
|
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: Oct4 | Statement: [Yamanaka factors, hasMember, Oct4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oct4 Context triple: [Yamanaka factors, hasMember, Oct4]
-
A.
Hoxa
Hoxa is a small settlement on the Orkney island of South Ronaldsay in Scotland, known for its coastal setting and nearby historic sites.
-
B.
Nissalke
Nissalke is the surname of Tom Nissalke, an American professional basketball coach known for his stints in the NBA and ABA.
-
C.
Dulbecco
Dulbecco is the surname of Renato Dulbecco, an Italian virologist and Nobel Prize laureate recognized for his pioneering work on the interaction between viruses and the genetic material of cells.
-
D.
Pontin
Pontin is a surname that appears as a spelling variant of the more common surname Ponting.
-
E.
Tetro
Tetro is a 2009 drama film directed by Francis Ford Coppola, in which Maribel Verdú plays a key supporting role in a story about fractured family relationships and artistic rivalry in Buenos Aires.
- 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: Oct4 Triple: [Yamanaka factors, hasMember, Oct4]
Generated description
Oct4 is a key pluripotency transcription factor essential for maintaining and reprogramming stem cells to an undifferentiated state.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oct4 Target entity description: Oct4 is a key pluripotency transcription factor essential for maintaining and reprogramming stem cells to an undifferentiated state.
-
A.
Hoxa
Hoxa is a small settlement on the Orkney island of South Ronaldsay in Scotland, known for its coastal setting and nearby historic sites.
-
B.
Nissalke
Nissalke is the surname of Tom Nissalke, an American professional basketball coach known for his stints in the NBA and ABA.
-
C.
Dulbecco
Dulbecco is the surname of Renato Dulbecco, an Italian virologist and Nobel Prize laureate recognized for his pioneering work on the interaction between viruses and the genetic material of cells.
-
D.
Pontin
Pontin is a surname that appears as a spelling variant of the more common surname Ponting.
-
E.
Tetro
Tetro is a 2009 drama film directed by Francis Ford Coppola, in which Maribel Verdú plays a key supporting role in a story about fractured family relationships and artistic rivalry in Buenos Aires.
- 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_69d6ada707008190aaec1238117c9379 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9547d6df4819080db8415d386ed38 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f655801cac8190b1f9a72f8fed0399 |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f6566f40c08190baec227fb660c948 |
completed | May 2, 2026, 7:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f657aa1bf48190a884e0dfce31e30e |
completed | May 2, 2026, 7:59 p.m. |
Created at: April 8, 2026, 9:57 p.m.