Triple
T20333411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucerne (her mother) |
E492540
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Ren
Ren is the child of Lucerne, a character likely situated within a fictional or narrative setting where family relationships are central to the story.
|
E1426904
|
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: Ren | Statement: [Lucerne (her mother), hasChild, Ren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ren Context triple: [Lucerne (her mother), hasChild, Ren]
-
A.
Ren
Ren is a central character in Margaret Atwood’s dystopian MaddAddam trilogy, known for her experiences as a sex worker and survivor in a bioengineered, post-apocalyptic world.
-
B.
Ren
Ren is a Chinese surname most prominently associated with Ren Zhengfei, the founder of Huawei.
-
C.
Ren
Ren is a short, informal diminutive of the given name Lauren.
-
D.
REN
REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
-
E.
Re
Re is a small village in the municipality of Gloppen in Vestland county, Norway.
- 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: Ren Triple: [Lucerne (her mother), hasChild, Ren]
Generated description
Ren is the child of Lucerne, a character likely situated within a fictional or narrative setting where family relationships are central to the story.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ren Target entity description: Ren is the child of Lucerne, a character likely situated within a fictional or narrative setting where family relationships are central to the story.
-
A.
Ren
Ren is a central character in Margaret Atwood’s dystopian MaddAddam trilogy, known for her experiences as a sex worker and survivor in a bioengineered, post-apocalyptic world.
-
B.
Ren
Ren is a Chinese surname most prominently associated with Ren Zhengfei, the founder of Huawei.
-
C.
Ren
Ren is a short, informal diminutive of the given name Lauren.
-
D.
REN
REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
-
E.
Re
Re is a small village in the municipality of Gloppen in Vestland county, Norway.
- 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_69e0b4a1a09881908d97270d6971a25a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e677e94e2481908898e0a3513e1209 |
completed | April 20, 2026, 7 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a087079a3888190818fa9e939b2f498 |
completed | May 16, 2026, 1:26 p.m. |
| NEDg | Description generation | batch_6a0871ee9b7081909772928d2cdfe317 |
completed | May 16, 2026, 1:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08727340a8819098be1ab14e4864a1 |
completed | May 16, 2026, 1:34 p.m. |
Created at: April 16, 2026, 11:22 a.m.