Triple
T27773021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Susan McAlester |
E701806
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object |
Janice Higgins
Janice Higgins is a professional colleague and research collaborator of Dr. Susan McAlester, likely working in a related academic or scientific field.
|
E1826359
|
NE FINISHED |
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: Janice Higgins | Statement: [Dr. Susan McAlester, collaboratesWith, Janice Higgins]
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: Janice Higgins Triple: [Dr. Susan McAlester, collaboratesWith, Janice Higgins]
Generated description
Janice Higgins is a professional colleague and research collaborator of Dr. Susan McAlester, likely working in a related academic or scientific field.
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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f63797ab708190876c93bc93b05043 |
completed | May 2, 2026, 5:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1cb6bff9ac8190aed95ae1bc50d66e |
completed | May 31, 2026, 10:31 p.m. |
| NEDg | Description generation | batch_6a1cbaaa69348190a4e8de0490e66edf |
completed | May 31, 2026, 10:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1cbb5d90ec819093705eae50314f33 |
completed | May 31, 2026, 10:51 p.m. |
Created at: April 27, 2026, 4:36 p.m.