Triple
T26303670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Meaning of Truth |
E661621
|
entity |
| Predicate | hasNotableChapter |
P6720
|
FINISHED |
| Object |
“Humanism and Truth”
“Humanism and Truth” is a key chapter in William James’s philosophical work that articulates his pragmatist account of truth through a human-centered, experiential perspective.
|
E1717457
|
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: “Humanism and Truth” | Statement: [The Meaning of Truth, hasNotableChapter, “Humanism and Truth”]
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: “Humanism and Truth” Triple: [The Meaning of Truth, hasNotableChapter, “Humanism and Truth”]
Generated description
“Humanism and Truth” is a key chapter in William James’s philosophical work that articulates his pragmatist account of truth through a human-centered, experiential perspective.
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_69ee812dacfc81908484aade9120fba9 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60ee166a88190b3a02ec60e8479fe |
completed | May 2, 2026, 2:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a118fce78488190a441368f0700b817 |
completed | May 23, 2026, 11:30 a.m. |
| NEDg | Description generation | batch_6a1190713f4c819082a89700881a3c46 |
completed | May 23, 2026, 11:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a119148a52c8190ab07b136673ee956 |
completed | May 23, 2026, 11:36 a.m. |
Created at: April 26, 2026, 10:17 p.m.