Triple
T14470122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tayran Ababil |
E358816
|
entity |
| Predicate | mentionedInChapterNumber |
P36548
|
FINISHED |
| Object | 105 |
—
|
LITERAL 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: 105 | Statement: [Tayran Ababil, mentionedInChapterNumber, 105]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mentionedInChapterNumber Context triple: [Tayran Ababil, mentionedInChapterNumber, 105]
-
A.
chapterNumber
Indicates the specific ordinal position a chapter occupies within a larger ordered work, such as a book or document.
-
B.
foundInChapter
chosen
Indicates that something (such as a concept, section, or element) is contained within or occurs in a specific chapter.
-
C.
appearsInBookNumber
Indicates that an entity is featured or mentioned in a specific book identified by its number within a series or collection.
-
D.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
E.
chapterOn
Indicates that one entity (typically a chapter) is about, discusses, or focuses on the subject represented by another entity.
- F. None of above.
Provenance (3 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91f969788190a5114f92d7159aae |
completed | April 14, 2026, 7:14 p.m. |
| PD | Predicate disambiguation | batch_69de5c42bd3c81909a62acf30cc24d1e |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:20 a.m.