Triple
T6854793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marquess |
E158109
|
entity |
| Predicate | femaleAddressedAs |
P72639
|
FINISHED |
| Object | Lady |
—
|
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: Lady | Statement: [Marquess, femaleAddressedAs, Lady]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleAddressedAs Context triple: [Marquess, femaleAddressedAs, Lady]
-
A.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
B.
femaleAbbreviation
Indicates that one entity is an abbreviation or shortened form specifically denoting a female version of another entity.
-
C.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
-
D.
hasFemaleRecipients
Indicates that the subject entity has one or more recipients who are female.
-
E.
hasGenderOfRecipients
Indicates the gender category or composition of the recipients involved in a given relationship or action.
- F. None of above. chosen
Provenance (4 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_69c6882fae988190864cbba788c5ebb4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d86d5a54819088537ada9f8d1105 |
completed | March 27, 2026, 7:20 p.m. |
| PD | Predicate disambiguation | batch_69c6d0a12834819097d7e6c0b823745e |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1668a7c8190ae93951f9ba2df10 |
completed | March 27, 2026, 6:50 p.m. |
Created at: March 27, 2026, 2:20 p.m.