Triple
T38384643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Well-Manicured Man |
E899544
|
entity |
| Predicate | relationshipWithMulder |
P202970
|
FINISHED |
| Object | reluctant informant |
—
|
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: reluctant informant | Statement: [The Well-Manicured Man, relationshipWithMulder, reluctant informant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithMulder Context triple: [The Well-Manicured Man, relationshipWithMulder, reluctant informant]
-
A.
relationshipWithFoxMulder
chosen
Indicates that an entity has some form of relationship or connection with Fox Mulder.
-
B.
relationshipWithDanaScully
Indicates having some form of personal or professional relationship with Dana Scully.
-
C.
relationshipToJaneRizzoli
Indicates the specific familial, social, or professional relationship that one entity has to Jane Rizzoli.
-
D.
relationshipToShawnSpencer
Indicates the specific type of personal or social relationship an entity has with Shawn Spencer.
-
E.
relationshipToSelinaMeyer
Indicates the specific type of personal or professional relationship an entity has with Selina Meyer.
- 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_69f76e5c9b808190b486523f5c2f817d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:31 p.m.