Triple
T36864071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nelson Davis |
E911021
|
entity |
| Predicate | freeTextDescription |
P11875
|
FINISHED |
| Object |
Nelson Davis was the second husband of abolitionist and Underground Railroad conductor Harriet Tubman, with whom he shared a long marriage after the Civil War.
Nelson Davis was an American Civil War veteran and laborer best known as the longtime second husband and companion of famed abolitionist Harriet Tubman in her later years.
|
E2203282
|
NE FINISHED |
How this triple was built (3 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: Nelson Davis was the second husband of abolitionist and Underground Railroad conductor Harriet Tubman, with whom he shared a long marriage after the Civil War. | Statement: [Nelson Davis, freeTextDescription, Nelson Davis was the second husband of abolitionist and Underground Railroad conductor Harriet Tubman, with whom he shared a long marriage after the Civil War.]
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: Nelson Davis was the second husband of abolitionist and Underground Railroad conductor Harriet Tubman, with whom he shared a long marriage after the Civil War. Triple: [Nelson Davis, freeTextDescription, Nelson Davis was the second husband of abolitionist and Underground Railroad conductor Harriet Tubman, with whom he shared a long marriage after the Civil War.]
Generated description
Nelson Davis was an American Civil War veteran and laborer best known as the longtime second husband and companion of famed abolitionist Harriet Tubman in her later years.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: freeTextDescription Context triple: [Nelson Davis, freeTextDescription, Nelson Davis was the second husband of abolitionist and Underground Railroad conductor Harriet Tubman, with whom he shared a long marriage after the Civil War.]
-
A.
describedThrough
Indicates that something is explained, represented, or conveyed by means of a particular medium, method, or description.
-
B.
hasDescription
chosen
Indicates that an entity is associated with a textual description that explains or characterizes it.
-
C.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
D.
definitionText
Indicates the textual content that provides the definition or explanatory meaning of a concept, term, or entity.
-
E.
designDescription
Indicates that an entity has a textual explanation or summary of its design, structure, or intended configuration.
- F. None of above.
Provenance (6 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_69f76e80f6f0819091cba8e19b269615 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3dfae204c08190a018b4d2bf0a7d3c |
completed | June 26, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_6a3e00adfc18819095a0c52aa7e20eaa |
completed | June 26, 2026, 4:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3e0729bf6c81908d34c6c8b24571fc |
completed | June 26, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69f7cf7890008190a8bc355ff2d61c86 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:13 p.m.