Triple
T32240134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mannering family |
E823589
|
entity |
| Predicate | knownThroughCharacter |
P69963
|
FINISHED |
| Object | Philip Mannering |
—
|
NE NERFINISHED |
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: Philip Mannering | Statement: [Mannering family, knownThroughCharacter, Philip Mannering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownThroughCharacter Context triple: [Mannering family, knownThroughCharacter, Philip Mannering]
-
A.
knownForStoryline
Indicates that an entity is recognized or notable specifically for its narrative or storyline.
-
B.
recognizedThrough
Indicates that something becomes known, identified, or acknowledged by means of a particular method, medium, or process.
-
C.
knownIn
Indicates that an entity is recognized, acknowledged, or familiar within a particular context, domain, or group.
-
D.
knownPrimarilyThrough
chosen
Indicates that one entity is chiefly recognized, identified, or made familiar to others by means of another entity (such as a work, role, medium, or context).
-
E.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
- 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_69f3490c140481908ed53b98b561eaa1 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f71996e1a48190ac59a1d66d7c44e8 |
completed | May 3, 2026, 9:47 a.m. |
| PD | Predicate disambiguation | batch_69f71820c6c88190ab38b4fa626d22cc |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 12:40 a.m.